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Scientific Python
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{
"metadata": {
"name": "scientific_python"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using Python as a MATLAB replacement\n",
"\n",
"This notebook is meant as an introduction to Python's essential scientific packages: Numpy, PIL, Matplotlib, and SciPy. It is recommended that you go through the first part of our Python introduction course first. This, and more Python learning material, is available on [our lab's wiki](http://gestaltrevision.be/wiki/python/python).\n",
"\n",
"**Author:** Maarten Demeyer \n",
"**Year:** 2014 \n",
"**Copyright:** Public Domain "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A Quick Recap\n",
"\n",
"### Data types\n",
"\n",
"Depending on what kind of values you want to store, Python variables can be of different data types. For instance:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_int = 5\n",
"print my_int, type(my_int)\n",
"\n",
"my_float = 5.0\n",
"print my_float, type(my_float)\n",
"\n",
"my_boolean = False\n",
"print my_boolean, type(my_boolean)\n",
"\n",
"my_string = 'hello'\n",
"print my_string, type(my_string)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"5 <type 'int'>\n",
"5.0 <type 'float'>\n",
"False <type 'bool'>\n",
"hello <type 'str'>\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Lists\n",
"\n",
"One useful data type is the list, which stores an **ordered**, **mutable** sequence of **any data type**, even **mixed**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_list = [my_int, my_float, my_boolean, my_string]\n",
"print type(my_list)\n",
"\n",
"for element in my_list:\n",
" print type(element)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<type 'list'>\n",
"<type 'int'>\n",
"<type 'float'>\n",
"<type 'bool'>\n",
"<type 'str'>\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To retrieve or change specific elements in a list, **indices** and **slicing** can be used.\n",
"\n",
"Indexing **starts at zero**. Slices **do not include the last element**."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print my_list[1]\n",
"\n",
"my_list[1] = 3.0\n",
"my_sublist = my_list[1:3]\n",
"\n",
"print my_sublist\n",
"print type(my_sublist)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"5.0\n",
"[3.0, False]\n",
"<type 'list'>\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Functions\n",
"\n",
"Re-usable pieces of code can be put into functions. Many pre-defined functions are available in Python packages.\n",
"\n",
"Functions can have both **required** and **optional** input arguments. When the function has no output argument, it returns **None**."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Function with a required and an optional argument\n",
"def regress(x, c=0, b=1):\n",
" return (x*b)+c\n",
"\n",
"print regress(5) # Only required argument\n",
"print regress(5, 10, 3) # Use argument order\n",
"print regress(5, b=3) # Specify the name to skip an optional argument"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"5\n",
"25\n",
"15\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Function without return argument\n",
"def divisible(a,b):\n",
" if a%b:\n",
" print str(a) + \" is not divisible by \" + str(b)\n",
" else:\n",
" print str(a) + \" is divisible by \" + str(b)\n",
"\n",
"divisible(9,3)\n",
"res = divisible(9,2)\n",
"print res"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"9 is divisible by 3\n",
"9 is not divisible by 2\n",
"None\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Function with multiple return arguments\n",
"def add_diff(a,b):\n",
" return a+b, a-b\n",
"\n",
"# Assigned as a tuple\n",
"res = add_diff(5,3)\n",
"print res\n",
"\n",
"# Directly unpacked to two variables\n",
"a,d = add_diff(5,3)\n",
"print a\n",
"print d"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(8, 2)\n",
"8\n",
"2\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Objects\n",
"\n",
"Every variable in Python is actually an object. Objects bundle **member variables** with tightly connected **member functions** that (typically) use these member variables. Lists are a good example of this."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_list = [1, False, 'boo']\n",
"my_list.append('extra element')\n",
"my_list.remove(False)\n",
"print my_list"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 'boo', 'extra element']\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The member variables in this case just contain the information on the elements in the list. They are 'hidden' and not intended to be used directly - you manipulate the list through its member functions.\n",
"\n",
"The functions above are **in-place** methods, changing the original list directly, and returning None. This is not always the case. Some member functions, for instance in strings, do not modify the original object, but return a second, modified object instead."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"return_arg = my_list.append('another one')\n",
"print return_arg\n",
"print my_list"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"None\n",
"[1, 'boo', 'extra element', 'another one']\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_string = 'kumbaya, milord'\n",
"return_arg = my_string.replace('lord', 'lard')\n",
"print return_arg\n",
"print my_string"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"kumbaya, milard\n",
"kumbaya, milord\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do you remember *why* list functions are in-place, while string functions are not?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numpy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Why we need Numpy \n",
"\n",
"\n",
"While lists are great, they are not very suitable for scientific computing. Consider this example:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"subj_length = [180.0,165.0,190.0,172.0,156.0]\n",
"subj_weight = [75.0,60.0,83.0,85.0,62.0]\n",
"subj_bmi = []\n",
"\n",
"# EXERCISE 1: Try to compute the BMI of each subject, as well as the average BMI across subjects\n",
"# BMI = weight/(length/100)**2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clearly, this is clumsy. MATLAB users would expect something like this to work:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"subj_bmi = subj_weight/(subj_length/100)**2 \n",
"mean_bmi = mean(subj_bmi)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "unsupported operand type(s) for /: 'list' and 'int'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-bf132dd4decd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msubj_bmi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubj_weight\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubj_length\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mmean_bmi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubj_bmi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'list' and 'int'"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But it doesn't. `/` and `**` are not defined for lists; nor does the `mean()` function exist. `+` and `*` are defined, but they mean something else. Do you remember what they do?\n",
"\n",
"\n",
"### The ndarray data type\n",
"\n",
"Enter Numpy, and its **ndarray** data type, allowing these *elementwise* computations on ordered sequences, and implementing a host of mathematical functions operating on them.\n",
"\n",
"Lists are converted to Numpy arrays through calling the `np.array()` **constructor** function, which takes a list and creates a new array object filled with the list's values."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"\n",
"# Create a numpy array from a list\n",
"subj_length = np.array([180.0,165.0,190.0,172.0,156.0])\n",
"subj_weight = np.array([75.0,60.0,83.0,85.0,62.0])\n",
"\n",
"print type(subj_length), type(subj_weight)\n",
"\n",
"# EXERCISE 2: Try to complete the program now!\n",
"# Hint: np.mean() computes the mean of a numpy array\n",
"# Note that unlike MATLAB, Python does not need the '.' before elementwise operators"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<type 'numpy.ndarray'> <type 'numpy.ndarray'>\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Numpy is a very large package, that we can't possibly cover completely. But we will cover enough to get you started."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### shape and dtype\n",
"\n",
"The most basic characteristics of a Numpy array are its shape and the data type of its elements, or dtype. For those of you who have worked in MATLAB before, this should be familiar."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Multi-dimensional lists are just nested lists\n",
"# This is clumsy to work with\n",
"my_nested_list = [[1,2,3],[4,5,6]]\n",
"\n",
"print my_nested_list\n",
"\n",
"print len(my_nested_list)\n",
"print my_nested_list[0]\n",
"print len(my_nested_list[0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[1, 2, 3], [4, 5, 6]]\n",
"2\n",
"[1, 2, 3]\n",
"3\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Numpy arrays handle multidimensionality better\n",
"arr = np.array(my_nested_list)\n",
"\n",
"print arr # nicer printing\n",
"print arr.shape # direct access to all dimension sizes\n",
"print arr.size # direct access to the total number of elements\n",
"print arr.ndim # direct access to the number of dimensions"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[1 2 3]\n",
" [4 5 6]]\n",
"(2, 3)\n",
"6\n",
"2\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The member variables **shape** and **size** contain the dimension lengths and the total number of elements, respectively, while **ndim** contains the number of dimensions. \n",
"\n",
"The shape is represented by a tuple, where the **last** dimension is the **inner** dimension representing the **columns** of a 2-D matrix. The first dimension is the top-level, outer dimension and represents the **rows** here. We could also make 3-D (or even higher-level) arrays:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr3d = np.array([ [[1,2,3],[4,5,6]] , [[7,8,9],[10,11,12]] ])\n",
"\n",
"print arr3d\n",
"\n",
"print arr3d.shape\n",
"print arr3d.size\n",
"print arr3d.ndim"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[[ 1 2 3]\n",
" [ 4 5 6]]\n",
"\n",
" [[ 7 8 9]\n",
" [10 11 12]]]\n",
"(2, 2, 3)\n",
"12\n",
"3\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now the last or inner dimension becomes the **layer** dimension. The inner lists of the constructor represent the values at that (row,column) coordinate of the various layers. Rows and columns remain the first two dimensions. Note how what we have here now, is **three** layers of **two-by-two** matrices. *Not* two layers of two-by-three matrices.\n",
"\n",
"This implies that dimension sizes are listed from **low to high** in the shape tuple. \n",
"\n",
"The second basic property of an array is its **dtype**. Contrary to list elements, numpy array elements are (typically) all of the same type."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# The type of a numpy array is always... numpy.ndarray\n",
"arr = np.array([[1,2,3],[4,5,6]])\n",
"print type(arr)\n",
"\n",
"# So, let's do a computation\n",
"print arr/2\n",
"\n",
"# Apparently we're doing our computations on integer elements!\n",
"# How do we find out?\n",
"print arr.dtype"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<type 'numpy.ndarray'>\n",
"[[0 1 1]\n",
" [2 2 3]]\n",
"int64\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# And how do we fix this?\n",
"arr = arr.astype('float') # Note: this is not an in-place function!\n",
"print arr.dtype\n",
"print arr/2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"float64\n",
"[[ 0.5 1. 1.5]\n",
" [ 2. 2.5 3. ]]\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Alternatively, we could have defined our dtype better from the start\n",
"arr = np.array([[1,2,3],[4,5,6]], dtype='float')\n",
"print arr.dtype\n",
"\n",
"arr = np.array([[1.,2.,3.],[4.,5.,6.]])\n",
"print arr.dtype"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"float64\n",
"float64\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To summarize, any numpy array is of the data type `numpy.ndarray`, but the data type of its elements can be set separately as its `dtype` member variable. It's a good idea to explicitly define the `dtype` when you create the array."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Indexing and slicing\n",
"\n",
"The same indexing and slicing operations used on lists can also be used on Numpy arrays. It is possible to perform computations on slices directly. \n",
"\n",
"But pay attention - Numpy arrays must have an **identical shape** if you want to combine them. There are some exceptions though, the most common being **scalar** operands."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.array([[1,2,3],[4,5,6],[7,8,9]], dtype='float')\n",
"\n",
"# Indexing and slicing\n",
"print arr[0,0] # or: arr[0][0]\n",
"print arr[:-1,0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1.0\n",
"[ 1. 4.]\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Elementwise computations on slices\n",
"# Remember, the LAST dimension is the INNER dimension\n",
"print arr[:,0] * arr[:,1]\n",
"print arr[0,:] * arr[1,:]\n",
"\n",
"# Note that you could never slice across rows like this in a nested list!"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 2. 20. 56.]\n",
"[ 4. 10. 18.]\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# This doesn't work\n",
"# print arr[1:,0] * arr[:,1]\n",
"\n",
"# And here's why:\n",
"print arr[1:,0].shape, arr[:,1].shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(2,) (3,)\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# This however does work. You can always use scalars as the other operand.\n",
"print arr[:,0] * arr[2,2]\n",
"\n",
"# Or, similarly:\n",
"print arr[:,0] * 9."
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 9. 36. 63.]\n",
"[ 9. 36. 63.]\n"
]
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As an **exercise**, can you create a 2x3 array containing the column-wise and the row-wise means of the original matrix, respectively? Without using a for-loop."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# EXERCISE 3: Create a 2x3 array containing the column-wise and the row-wise means of the original matrix\n",
"# Do not use a for-loop, and also do not use the np.mean() function for now.\n",
"arr = np.array([[1,2,3],[4,5,6],[7,8,9]], dtype='float')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This works, but it is still a bit clumsy. We will learn more efficient methods below."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Filling and manipulating arrays\n",
"\n",
"**Creating** arrays mustn't always be done by hand. The following functions are particularly common. Again, they are analogous to what you do in MATLAB."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 1-D array, filled with zeros\n",
"arr = np.zeros(3)\n",
"print arr\n",
"\n",
"# Multidimensional array of a given shape, filled with ones\n",
"# This automatically allows you to fill arrays with /any/ value\n",
"arr = np.ones((3,2))*5\n",
"print arr\n",
"\n",
"# Sequence from 1 to AND NOT including 16, in steps of 3\n",
"# Note that using a float input makes the dtype a float as well\n",
"# This is equivalent to np.array(range(1.,16.,3))\n",
"arr = np.arange(1.,16.,3)\n",
"print arr\n",
"\n",
"# Sequence from 1 to AND including 16, in 3 steps\n",
"# This always returns an array with dtype float\n",
"arr = np.linspace(1,16,3)\n",
"print arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 0. 0. 0.]\n",
"[[ 5. 5.]\n",
" [ 5. 5.]\n",
" [ 5. 5.]]\n",
"[ 1. 4. 7. 10. 13.]\n",
"[ 1. 8.5 16. ]\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Array of random numbers between 0 and 1, of a given shape\n",
"# Note that the inputs here are separate integers, not a tuple\n",
"arr = np.random.rand(5,2)\n",
"print arr\n",
"\n",
"# Array of random integers from 0 to AND NOT including 10, of a given shape\n",
"# Here the shape is defined as a tuple again\n",
"arr = np.random.randint(0,10,(5,2))\n",
"print arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0.74171431 0.9108704 ]\n",
" [ 0.25811013 0.45092435]\n",
" [ 0.70336307 0.12976941]\n",
" [ 0.90958252 0.29807502]\n",
" [ 0.48594021 0.41648359]]\n",
"[[3 8]\n",
" [4 4]\n",
" [8 3]\n",
" [4 0]\n",
" [3 4]]\n"
]
}
],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once we have an array, we may wish to **replicate** it to create a larger array. \n",
"\n",
"Here the concept of an **axis** becomes important, i.e., along which of the dimensions of the array are you working? **axis=0** corresponds to the **first** dimension of the shape tuple, **axis=-1** always corresponds to the **last** dimension (inner dimension; columns in case of 2D, layers in case of 3D)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr0 = np.array([[1,2],[3,4]])\n",
"print arr0\n",
"\n",
"# 'repeat' replicates elements along a given axis\n",
"# Each element is replicated directly after itself\n",
"arr = np.repeat(arr0, 3, axis=-1)\n",
"print arr\n",
"\n",
"# We may even specify the number of times each element should be repeated\n",
"# The length of the tuple should correspond to the dimension length\n",
"arr = np.repeat(arr0, (2,4), axis=0)\n",
"print arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[1 2]\n",
" [3 4]]\n",
"[[1 1 1 2 2 2]\n",
" [3 3 3 4 4 4]]\n",
"[[1 2]\n",
" [1 2]\n",
" [3 4]\n",
" [3 4]\n",
" [3 4]\n",
" [3 4]]\n"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print arr0\n",
"\n",
"# 'tile' replicates the array as a whole\n",
"# Use a tuple to specify the number of tilings along each dimensions\n",
"arr = np.tile(arr0, (2,4))\n",
"print arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[1 2]\n",
" [3 4]]\n",
"[[1 2 1 2 1 2 1 2]\n",
" [3 4 3 4 3 4 3 4]\n",
" [1 2 1 2 1 2 1 2]\n",
" [3 4 3 4 3 4 3 4]]\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 'meshgrid' is commonly used to create X and Y coordinate arrays from two vectors\n",
"# where each array contains the X or Y coordinates corresponding to a given pixel in an image\n",
"x = np.arange(10)\n",
"y = np.arange(5)\n",
"print x,y\n",
"\n",
"arrx, arry = np.meshgrid(x,y)\n",
"print arrx\n",
"print arry"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0 1 2 3 4 5 6 7 8 9] [0 1 2 3 4]\n",
"[[0 1 2 3 4 5 6 7 8 9]\n",
" [0 1 2 3 4 5 6 7 8 9]\n",
" [0 1 2 3 4 5 6 7 8 9]\n",
" [0 1 2 3 4 5 6 7 8 9]\n",
" [0 1 2 3 4 5 6 7 8 9]]\n",
"[[0 0 0 0 0 0 0 0 0 0]\n",
" [1 1 1 1 1 1 1 1 1 1]\n",
" [2 2 2 2 2 2 2 2 2 2]\n",
" [3 3 3 3 3 3 3 3 3 3]\n",
" [4 4 4 4 4 4 4 4 4 4]]\n"
]
}
],
"prompt_number": 28
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Concatenating** an array allows you to make several arrays into one."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr0 = np.array([[1,2],[3,4]])\n",
"arr1 = np.array([[5,6],[7,8]])\n",
"\n",
"# 'concatenate' requires an axis to perform its operation on\n",
"# The original arrays should be put in a tuple\n",
"arr = np.concatenate((arr0,arr1), axis=0)\n",
"print arr # as new rows\n",
"\n",
"arr = np.concatenate((arr0,arr1), axis=1)\n",
"print arr # as new columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[1 2]\n",
" [3 4]\n",
" [5 6]\n",
" [7 8]]\n",
"[[1 2 5 6]\n",
" [3 4 7 8]]\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Suppose we want to create a 3-D matrix from them,\n",
"# we have to create them as being three-dimensional\n",
"# (what happens if you don't?)\n",
"arr0 = np.array([[[1],[2]],[[3],[4]]])\n",
"arr1 = np.array([[[5],[6]],[[7],[8]]])\n",
"print arr0.shape, arr1.shape\n",
"arr = np.concatenate((arr0,arr1),axis=2)\n",
"print arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(2, 2, 1) (2, 2, 1)\n",
"[[[1 5]\n",
" [2 6]]\n",
"\n",
" [[3 7]\n",
" [4 8]]]\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# hstack, vstack, and dstack are short-hand functions\n",
"# which will automatically create these 'missing' dimensions\n",
"arr0 = np.array([[1,2],[3,4]])\n",
"arr1 = np.array([[5,6],[7,8]])\n",
"\n",
"# vstack() concatenates rows\n",
"arr = np.vstack((arr0,arr1))\n",
"print arr\n",
"\n",
"# hstack() concatenates columns\n",
"arr = np.hstack((arr0,arr1))\n",
"print arr\n",
"\n",
"# dstack() concatenates 2D arrays into 3D arrays\n",
"arr = np.dstack((arr0,arr1))\n",
"print arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[1 2]\n",
" [3 4]\n",
" [5 6]\n",
" [7 8]]\n",
"[[1 2 5 6]\n",
" [3 4 7 8]]\n",
"[[[1 5]\n",
" [2 6]]\n",
"\n",
" [[3 7]\n",
" [4 8]]]\n"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Their counterparts are the hsplit, vsplit, dsplit functions\n",
"# They take a second argument: how do you want to split\n",
"arr = np.random.rand(4,4)\n",
"print arr\n",
"print '--'\n",
"\n",
"# Splitting int equal parts \n",
"arr0,arr1 = hsplit(arr,2)\n",
"print arr0\n",
"print arr1\n",
"print '--'\n",
"\n",
"# Or, specify exact split points\n",
"arr0,arr1,arr2 = hsplit(arr,(1,2))\n",
"print arr0\n",
"print arr1\n",
"print arr2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0.27599328 0.12804176 0.33579253 0.54435602]\n",
" [ 0.85936248 0.71082484 0.63731495 0.99756099]\n",
" [ 0.10047941 0.78998524 0.63749261 0.95093453]\n",
" [ 0.56534435 0.8216909 0.71499875 0.09313218]]\n",
"--\n",
"[[ 0.27599328 0.12804176]\n",
" [ 0.85936248 0.71082484]\n",
" [ 0.10047941 0.78998524]\n",
" [ 0.56534435 0.8216909 ]]\n",
"[[ 0.33579253 0.54435602]\n",
" [ 0.63731495 0.99756099]\n",
" [ 0.63749261 0.95093453]\n",
" [ 0.71499875 0.09313218]]\n",
"--\n",
"[[ 0.27599328]\n",
" [ 0.85936248]\n",
" [ 0.10047941]\n",
" [ 0.56534435]]\n",
"[[ 0.12804176]\n",
" [ 0.71082484]\n",
" [ 0.78998524]\n",
" [ 0.8216909 ]]\n",
"[[ 0.33579253 0.54435602]\n",
" [ 0.63731495 0.99756099]\n",
" [ 0.63749261 0.95093453]\n",
" [ 0.71499875 0.09313218]]\n"
]
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we can easily **reshape** and **transpose** arrays"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr0 = np.arange(10)\n",
"print arr0\n",
"print '--'\n",
"\n",
"# 'reshape' does exactly what you would expect\n",
"# Make sure though that the total number of elements remains the same\n",
"arr = np.reshape(arr0,(5,2))\n",
"print arr\n",
"\n",
"# You can also leave one dimension blank by using -1 as a value\n",
"# Numpy will then compute for you how long this dimension should be\n",
"arr = np.reshape(arr0,(-1,5))\n",
"print arr\n",
"print '--'\n",
"\n",
"# 'transpose' allows you to switch around dimensions\n",
"# A tuple specifies the new order of dimensions\n",
"arr = np.transpose(arr,(1,0))\n",
"print arr\n",
"\n",
"# For simply transposing rows and columns, there is the short-hand form .T\n",
"arr = arr.T\n",
"print arr\n",
"print '--'\n",
"\n",
"# 'flatten' creates a 1D array out of everything\n",
"arr = arr.flatten()\n",
"print arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0 1 2 3 4 5 6 7 8 9]\n",
"--\n",
"[[0 1]\n",
" [2 3]\n",
" [4 5]\n",
" [6 7]\n",
" [8 9]]\n",
"[[0 1 2 3 4]\n",
" [5 6 7 8 9]]\n",
"--\n",
"[[0 5]\n",
" [1 6]\n",
" [2 7]\n",
" [3 8]\n",
" [4 9]]\n",
"[[0 1 2 3 4]\n",
" [5 6 7 8 9]]\n",
"--\n",
"[0 1 2 3 4 5 6 7 8 9]\n"
]
}
],
"prompt_number": 33
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Time for an exercise!** Can you write your own 'meshgrid3d' function, which returns the resulting 2D arrays as two layers of a 3D matrix, instead of two separate 2D arrays?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# EXERCISE 4: Create your own meshgrid3d function\n",
"# Like np.meshgrid(), it should take two vectors and replicate them; one into columns, the other into rows\n",
"# Unlike np.meshgrid(), it should return them as a single 3D array rather than 2D arrays\n",
"# ...do not use the np.meshgrid() function\n",
"\n",
"def meshgrid3d(xvec, yvec):\n",
" # fill in!\n",
"\n",
"xvec = np.arange(10)\n",
"yvec = np.arange(5)\n",
"xy = meshgrid3d(xvec, yvec)\n",
"print xy\n",
"print xy[:,:,0] # = first output of np.meshgrid()\n",
"print xy[:,:,1] # = second output of np.meshgrid()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### A few useful functions\n",
"\n",
"We can now handle arrays in any way we like, but we still don't know any operations to perform on them, other than the basic arithmetic operations. Luckily numpy implements a large collection of common computations. This is a very short review of some useful functions."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.random.rand(5)\n",
"print arr\n",
"\n",
"# Sorting and shuffling\n",
"res = arr.sort() \n",
"print arr # in-place!!!\n",
"print res\n",
"\n",
"res = np.random.shuffle(arr) \n",
"print arr # in-place!!!\n",
"print res"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 0.53201935 0.57819634 0.16985856 0.5446936 0.98530523]\n",
"[ 0.16985856 0.53201935 0.5446936 0.57819634 0.98530523]\n",
"None\n",
"[ 0.57819634 0.98530523 0.5446936 0.53201935 0.16985856]\n",
"None\n"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Min, max, mean, standard deviation\n",
"arr = np.random.rand(5)\n",
"print arr\n",
"\n",
"mn = np.min(arr)\n",
"mx = np.max(arr)\n",
"print mn, mx\n",
"\n",
"mu = np.mean(arr)\n",
"sigma = np.std(arr)\n",
"print mu, sigma"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 0.32931437 0.52008775 0.29728744 0.68548813 0.54189534]\n",
"0.297287438913 0.685488129026\n",
"0.474814606712 0.143957653614\n"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Some functions allow you to specify an axis to work along, in case of multidimensional arrays\n",
"arr2d = np.random.rand(3,5)\n",
"print arr2d\n",
"\n",
"print np.mean(arr2d, axis=0)\n",
"print np.mean(arr2d, axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0.73219162 0.59816746 0.67481492 0.09101851 0.42319253]\n",
" [ 0.67654097 0.77650845 0.94659466 0.59576357 0.53261734]\n",
" [ 0.68026625 0.32510923 0.91817799 0.56756805 0.83025812]]\n",
"[ 0.69633295 0.56659505 0.84652919 0.41811671 0.595356 ]\n",
"[ 0.50387701 0.705605 0.66427593]\n"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Trigonometric functions\n",
"# Note: Numpy works with radians units, not degrees\n",
"arr = np.random.rand(5)\n",
"print arr\n",
"\n",
"sn = np.sin(arr*2*np.pi)\n",
"cs = np.cos(arr*2*np.pi)\n",
"print sn\n",
"print cs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 0.77989143 0.12790275 0.4906743 0.40220502 0.74017857]\n",
"[-0.98241484 0.71988506 0.05856157 0.57652064 -0.99809655]\n",
"[ 0.1867112 0.69409329 -0.9982838 -0.81708259 -0.06167068]\n"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Exponents and logarithms\n",
"arr = np.random.rand(5)\n",
"print arr\n",
"\n",
"xp = np.exp(arr)\n",
"print xp\n",
"print np.log(xp)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 0.37390996 0.34476585 0.56485829 0.81188406 0.80976676]\n",
"[ 1.45340628 1.41165935 1.75919847 2.25214718 2.24738375]\n",
"[ 0.37390996 0.34476585 0.56485829 0.81188406 0.80976676]\n"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Rounding\n",
"arr = np.random.rand(5)\n",
"print arr\n",
"\n",
"print arr*5\n",
"print np.round(arr*5)\n",
"print np.floor(arr*5)\n",
"print np.ceil(arr*5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 0.28849423 0.94006547 0.2291144 0.44195437 0.28959184]\n",
"[ 1.44247117 4.70032737 1.145572 2.20977185 1.44795918]\n",
"[ 1. 5. 1. 2. 1.]\n",
"[ 1. 4. 1. 2. 1.]\n",
"[ 2. 5. 2. 3. 2.]\n"
]
}
],
"prompt_number": 42
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A complete list of all numpy functions can be found at [the Numpy website](http://docs.scipy.org/doc/numpy/reference/). Or, a google search for 'numpy tangens', 'numpy median' or similar will usually get you there as well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### A small exercise\n",
"\n",
"Remember how you were asked to create a 2x3 array containing the column-wise and the row-wise means of a matrix above? We now have the knowledge to do this far shorter. Use a concatenation function and a statistical function to obtain the same thing!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# EXERCISE 5: Make a better version of Exercise 3 with what you've just learned\n",
"arr = np.array([[1,2,3],[4,5,6],[7,8,9]], dtype='float')\n",
"\n",
"# What we had:\n",
"print np.array([(arr[:,0]+arr[:,1]+arr[:,2])/3,(arr[0,:]+arr[1,:]+arr[2,:])/3])\n",
"\n",
"# Now the new version:"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 2. 5. 8.]\n",
" [ 4. 5. 6.]]\n"
]
}
],
"prompt_number": 43
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### A bit harder: The Gabor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Gabor patch is the product of a sinusoidal grating and a Gaussian. If we ignore orientation and just create a vertically oriented Gabor, the grating luminance (bounded between -1 and 1) is created by:\n",
"\n",
"$grating = \\sin(xf)$\n",
"\n",
"where $x$ is the $x$ coordinate of a pixel, and $f$ is the frequency of the sine wave (how many peaks per $2 \\pi$ coordinate units). A simple 2D Gaussian luminance profile (bounded between 0 and 1) with its peak at coordinate $(0,0)$ and a variance of $1$ is given by:\n",
"\n",
"$gaussian = e^{-(x^2+y^2)/2}$\n",
"\n",
"where $x$ and $y$ are again the $x$ and $y$ coordinates of a pixel. The Gabor luminance (bounded between -1 and 1) for any pixel then equals:\n",
"\n",
"$gabor = grating \\times gaussian$\n",
"\n",
"To visualize this, these are the grating, the Gaussian, and the Gabor, respectively (at maximal contrast):\n",
"<img src=\"files/gabor.png\">\n",
"\n",
"Now you try to create a 100x100 pixel image of a Gabor. Use $x$ and $y$ coordinate values ranging from $-\\pi$ to $\\pi$, and a frequency of 10 for a good-looking result."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# EXERCISE 6: Create a Gabor patch of 100 by 100 pixels\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Step 1: Define the 1D coordinate values\n",
"# Tip: use 100 equally spaced values between -np.pi and np.pi\n",
"\n",
"# Step 2: Create the 2D x and y coordinate arrays\n",
"# Tip: use np.meshgrid()\n",
"\n",
"# Step 3: Create the grating\n",
"# Tip: Use a frequency of 10\n",
"\n",
"# Step 4: Create the Gaussian\n",
"# Tip: use np.exp() to compute a power of e\n",
"\n",
"# Step 5: Create the Gabor\n",
"\n",
"# Visualize your result\n",
"# (we will discuss how this works later)\n",
"plt.figure(figsize=(15,5))\n",
"plt.subplot(131)\n",
"plt.imshow(grating, cmap='gray')\n",
"plt.subplot(132)\n",
"plt.imshow(gaussian, cmap='gray')\n",
"plt.subplot(133)\n",
"plt.imshow(gabor, cmap='gray')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Boolean indexing\n",
"\n",
"The dtype of a Numpy array can also be boolean, that is, `True` or `False`. It is then particularly convenient that given an array of the same shape, these boolean arrays can be used to **index other arrays**."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Check whether each element of a 2x2 array is greater than 0.5\n",
"arr = np.random.rand(2,2)\n",
"print arr\n",
"res = arr>0.5\n",
"print res\n",
"print '--'\n",
" \n",
"# Analogously, check it against each element of a second 2x2 array\n",
"arr2 = np.random.rand(2,2)\n",
"print arr2\n",
"res = arr>arr2\n",
"print res"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0.40594786 0.64510352]\n",
" [ 0.35200933 0.10239218]]\n",
"[[False True]\n",
" [False False]]\n",
"--\n",
"[[ 0.11467608 0.356683 ]\n",
" [ 0.01329068 0.89249923]]\n",
"[[ True True]\n",
" [ True False]]\n"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# We can use these boolean arrays as indices into other arrays!\n",
"\n",
"# Add 0.5 to any element smaller than 0.5\n",
"arr = np.random.rand(2,2)\n",
"print arr\n",
"res = arr<0.5\n",
"print res\n",
"arr[res] = arr[res]+0.5\n",
"print arr\n",
"\n",
"# Or, shorter:\n",
"arr[arr<0.5] = arr[arr<0.5] + 0.5\n",
"\n",
"# Or, even shorter:\n",
"arr[arr<0.5] += 0.5"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 1.53582014e-01 3.21366067e-04]\n",
" [ 3.99344474e-01 9.79744870e-01]]\n",
"[[ True True]\n",
" [ True False]]\n",
"[[ 0.65358201 0.50032137]\n",
" [ 0.89934447 0.97974487]]\n"
]
}
],
"prompt_number": 45
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While it is possible to do multiplication and addition on boolean values (this will convert them to ones and zeros), the proper way of doing elementwise boolean logic is to use **boolean operators**: **and, or, xor, not**."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.array([[1,2,3],[4,5,6]])\n",
"\n",
"# The short-hand forms for elementwise boolean operators are: & | ~ ^\n",
"# Use parentheses around such expressions\n",
"res = (arr<4) & (arr>1)\n",
"print res\n",
"print '--'\n",
"\n",
"res = (arr<2) | (arr==5)\n",
"print res\n",
"print '--'\n",
"\n",
"res = (arr>3) & ~(arr==6)\n",
"print res\n",
"print '--'\n",
"\n",
"res = (arr>3) ^ (arr<5)\n",
"print res"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[False True True]\n",
" [False False False]]\n",
"--\n",
"[[ True False False]\n",
" [False True False]]\n",
"--\n",
"[[False False False]\n",
" [ True True False]]\n",
"--\n",
"[[ True True True]\n",
" [False True True]]\n"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# To convert boolean indices to normal integer indices, use the 'nonzero' function\n",
"print res\n",
"print np.nonzero(res)\n",
"print '--'\n",
"\n",
"# Separate row and column indices\n",
"print np.nonzero(res)[0]\n",
"print np.nonzero(res)[1]\n",
"print '--'\n",
"\n",
"# Or stack and transpose them to get index pairs\n",
"pairs = np.vstack(np.nonzero(res)).T\n",
"print pairs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ True True True]\n",
" [False True True]]\n",
"(array([0, 0, 0, 1, 1]), array([0, 1, 2, 1, 2]))\n",
"--\n",
"[0 0 0 1 1]\n",
"[0 1 2 1 2]\n",
"--\n",
"[[0 0]\n",
" [0 1]\n",
" [0 2]\n",
" [1 1]\n",
" [1 2]]\n"
]
}
],
"prompt_number": 47
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vectorizing a simulation\n",
"\n",
"Numpy is excellent at making programs that involve iterative operations more efficient. This then requires you to re-imagine the problem as an array of values, rather than values that change with each loop iteration.\n",
"\n",
"For instance, imagine the following situation: You throw a die continuously until you either encounter the sequence \u2018123\u2019 or \u2018111\u2019. Which one can be expected to occur sooner? This could be proven mathematically, but in practice it is often faster to do a simulation instead of working out an analytical solution. \n",
"\n",
"We could just use two nested for-loops:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"\n",
"# We will keep track of the sum of first occurence positions,\n",
"# as well as the number of positions entered into this sum.\n",
"# This way we can compute the mean.\n",
"sum111 = 0.\n",
"n111 = 0.\n",
"sum123 = 0.\n",
"n123 = 0.\n",
"\n",
"for sim in range(5000):\n",
" \n",
" # Keep track of how far along we are in finding a given pattern\n",
" d111 = 0\n",
" d123 = 0\n",
" \n",
" for throw in range(2000):\n",
" \n",
" # Throw a die\n",
" die = np.random.randint(1,7)\n",
" \n",
" # 111 case\n",
" if d111==3:\n",
" pass\n",
" elif die==1 and d111==0:\n",
" d111 = 1\n",
" elif die==1 and d111==1:\n",
" d111 = 2\n",
" elif die==1 and d111==2:\n",
" d111 = 3\n",
" sum111 = sum111 + throw\n",
" n111 = n111 + 1 \n",
" else:\n",
" d111 = 0\n",
" \n",
" # 123 case\n",
" if d123==3:\n",
" pass\n",
" elif die==1:\n",
" d123 = 1\n",
" elif die==2 and d123==1:\n",
" d123 = 2\n",
" elif die==3 and d123==2:\n",
" d123 = 3\n",
" sum123 = sum123 + throw\n",
" n123 = n123 + 1\n",
" else:\n",
" d123 = 0\n",
" \n",
" # Don't continue if both have been found\n",
" if d111==3 and d123==3:\n",
" break\n",
"\n",
"# Compute the averages\n",
"avg111 = sum111/n111\n",
"avg123 = sum123/n123\n",
"print avg111, avg123 \n",
"\n",
"# ...can you spot the crucial difference between both patterns?"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"260.3634 212.9018\n"
]
}
],
"prompt_number": 48
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However this is inefficient and makes the code unwieldy. Vectorized solutions are usually preferred.\n",
"\n",
"Try to run these 5000 simulations using Numpy, **without any loops**, and see whether the result is the same. Use a maximal die-roll sequence length of 2000, and just assume that both '123' and '111' will occur before the end of any sequence.\n",
"\n",
"You will have to make use of 2D arrays and boolean logic. A quick solution to find the first occurence in a boolean array is to use **argmax** - use the only Numpy documentation to find out how to use it.\n",
"\n",
"**Vectorizing problems is a crucial skill in scientific computing!**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# EXERCISE 7: Vectorize the above program\n",
"\n",
"# You get these lines for free...\n",
"import numpy as np\n",
"throws = np.random.randint(1,7,(5000,2000))\n",
"one = (throws==1)\n",
"two = (throws==2)\n",
"three = (throws==3)\n",
"\n",
"# Find out where all the 111 and 123 sequences occur\n",
"find111 = \n",
"find123 = \n",
"\n",
"# Then at what index they /first/ occur in each sequence\n",
"first111 = \n",
"first123 = \n",
"\n",
"# Compute the average first occurence location for both situations\n",
"avg111 = \n",
"avg123 = \n",
"\n",
"# Print the result\n",
"print avg111, avg123"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this particular example, the nested for-loop solution does have the advantage that it can 'break' out of the die throwing sequence when first occurences of both patterns have been found, whereas Numpy will always generate complete sequences of 2000 rolls. \n",
"\n",
"Remove the `break` statement in the first solution to see what the speed difference would have been if both programs were truly doing the same thing!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PIL: the Python Imaging Library\n",
"\n",
"As vision scientists, images are a natural stimulus to work with. The Python Imaging Library will help us handle images, similar to the Image Processing toolbox in MATLAB. Note that PIL itself has nowadays been superseded by **Pillow**, for which an excellent documentation can be found [here](http://pillow.readthedocs.org/). The module to import is however still called 'PIL'. In practice, we will mostly use its **Image** module."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from PIL import Image"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 51
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loading and showing images\n",
"\n",
"The image we will use for this example code should be in the same directory as this file. But really, any color image will do, as long as you put it in the same directory as this notebook, and change the filename string in the code to correspond with the actual image filename."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Opening an image is simple enough:\n",
"# Construct an Image object with the filename as an argument\n",
"im = Image.open('python.jpg')\n",
"\n",
"# It is now represented as an object of the 'JpegImageFile' type\n",
"print im\n",
"\n",
"# There are some useful member variables we can inspect here\n",
"print im.format # format in which the file was saved\n",
"print im.size # pixel dimensions\n",
"print im.mode # luminance/color model used\n",
"\n",
"# We can even display it\n",
"# NOTE this is not perfect; meant for debugging\n",
"im.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x768 at 0x34022D8>\n",
"JPEG\n",
"(1024, 768)\n",
"RGB\n"
]
}
],
"prompt_number": 52
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the `im.show()` call does not work well on your system, use this function instead to show images in a separate window. Note, you must always close the window before you can continue using the notebook. \n",
"\n",
"(`Tkinter` is a package to write graphical user interfaces in Python, we will not discuss it here)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Alternative quick-show method\n",
"from Tkinter import Tk, Button\n",
"from PIL import ImageTk\n",
"\n",
"def alt_show(im):\n",
" win = Tk()\n",
" tkimg = ImageTk.PhotoImage(im)\n",
" Button(image=tkimg).pack()\n",
" win.mainloop( )\n",
"\n",
"alt_show(im)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 53
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once we have opened the image in PIL, we can convert it to a Numpy object."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# We can convert PIL images to an ndarray!\n",
"arr = np.array(im)\n",
"print arr.dtype # uint8 = unsigned 8-bit integer (values 0-255 only)\n",
"print arr.shape # Why do we have three layers?\n",
"\n",
"# Let's make it a float-type for doing computations\n",
"arr = arr.astype('float')\n",
"print arr.dtype\n",
"\n",
"# This opens up unlimited possibilities for image processing!\n",
"# For instance, let's make this a grayscale image, and add white noise\n",
"max_noise = 50\n",
"arr = np.mean(arr,-1)\n",
"noise = (np.random.rand(arr.shape[0],arr.shape[1])-0.5)*2\n",
"arr = arr + noise*max_noise\n",
"\n",
"# Make sure we don't exceed the 0-255 limits of a uint8\n",
"arr[arr<0] = 0\n",
"arr[arr>255] = 255"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"uint8\n",
"(768, 1024, 3)\n",
"float64\n"
]
}
],
"prompt_number": 54
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The conversion back to PIL is easy as well"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# When going back to PIL, it's a good idea to explicitly \n",
"# specify the right dtype and the mode.\n",
"# Because automatic conversions might mess things up\n",
"arr = arr.astype('uint8')\n",
"imn = Image.fromarray(arr, mode='L')\n",
"print imn.format\n",
"print imn.size\n",
"print imn.mode # L = greyscale\n",
"imn.show() # or use alt_show() from above if show() doesn't work well for you\n",
"\n",
"# Note that /any/ 2D or 2Dx3 numpy array filled with values between 0 and 255\n",
"# can be converted to an image object in this way"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"None\n",
"(1024, 768)\n",
"L\n"
]
}
],
"prompt_number": 55
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Resizing, rotating, cropping and converting\n",
"\n",
"The main operations of the PIL Image module you will probably use, are its **resizing** and **conversion** capabilities."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"im = Image.open('python.jpg')\n",
"\n",
"# Make the image smaller\n",
"ims = im.resize((800,600))\n",
"ims.show()\n",
"\n",
"# Or you could even make it larger\n",
"# The resample argument allows you to specify the method used\n",
"iml = im.resize((1280,1024), resample=Image.BILINEAR)\n",
"iml.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Rotation is similar (unit=degrees)\n",
"imr = im.rotate(10, resample=Image.BILINEAR, expand=False)\n",
"imr.show()\n",
"\n",
"# If we want to lose the black corners, we can crop (unit=pixels)\n",
"imr = imr.crop((100,100,924,668))\n",
"imr.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 'convert' allows conversion between different color models\n",
"# The most important here is between 'L' (luminance) and 'RGB' (color)\n",
"imbw = im.convert('L')\n",
"imbw.show()\n",
"print imbw.mode\n",
"\n",
"imrgb = imbw.convert('RGB')\n",
"imrgb.show()\n",
"print imrgb.mode\n",
"\n",
"# Note that the grayscale conversion of PIL is more sophisticated\n",
"# than simply averaging the three layers in Numpy (it is a weighted average)\n",
"\n",
"# Also note that the color information is effectively lost after converting to L"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"L\n",
"RGB"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 58
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Advanced\n",
"\n",
"The **ImageFilter** module implements several types of filters to execute on any image. You can also define your own."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from PIL import Image, ImageFilter\n",
"\n",
"im = Image.open('python.jpg')\n",
"imbw = im.convert('L')\n",
"\n",
"# Contour detection filter\n",
"imf = imbw.filter(ImageFilter.CONTOUR)\n",
"imf.show()\n",
"\n",
"# Blurring filter\n",
"imf = imbw.filter(ImageFilter.GaussianBlur(radius=3))\n",
"imf.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 59
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarly, you can import the **ImageDraw** module to draw shapes and text onto an image."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from PIL import Image, ImageDraw\n",
"\n",
"im = Image.open('python.jpg')\n",
"\n",
"# You need to attach a drawing object to the image first\n",
"imd = ImageDraw.Draw(im)\n",
"\n",
"# Then you work on this object\n",
"imd.rectangle([10,10,100,100], fill=(255,0,0))\n",
"imd.line([(200,200),(200,600)], width=10, fill=(0,0,255))\n",
"imd.text([500,500], 'Python', fill=(0,255,0))\n",
"\n",
"# The results are automatically applied to the Image object\n",
"im.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 60
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Saving\n",
"\n",
"Finally, you can of course save these image objects back to a file on the disk."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# PIL will figure out the file type by the extension\n",
"im.save('python.bmp')\n",
"\n",
"# There are also further options, like compression quality (0-100)\n",
"im.save('python_bad.jpg', quality=5)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 61
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"We mentioned that the conversion to grayscale in PIL is not just a simple averaging of the RGB layers. Can you visualize as an image what the difference in result looks like, when comparing a simple averaging to a PIL grayscale conversion?\n",
"\n",
"Pixels that are less luminant in the plain averaging method should be displayed in red, with a luminance depending on the size of the difference. Pixels that are more luminant when averaging in Numpy should similarly be displayed in green.\n",
"\n",
"**Hint: you will have to make use of Boolean indexing.**\n",
"\n",
"As an extra, try to maximize the contrast in your image, so that all values from 0-255 are used. \n",
"\n",
"As a second extra, save the result as PNG files of three different sizes (large, medium, small), at respectively the full image resolution, half of the image size, and a quarter of the image size."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# EXERCISE 8: Visualize the difference between the PIL conversion to grayscale, and a simple averaging of RGB\n",
"# Display pixels where the average is LESS luminant in red, and where it is MORE luminant in shades green\n",
"# The luminance of these colors should correspond to the size of the difference\n",
"#\n",
"# Extra 1: Maximize the overall contrast in your image\n",
"#\n",
"# Extra 2: Save as three PNG files, of different sizes (large, medium, small)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 62
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Matplotlib\n",
"\n",
"While PIL is useful for processing photographic images, it falls short for creating data plots and other kinds of schematic figures. **Matplotlib** offers a far more advanced solution for this, specifically through its **pyplot** module.\n",
"\n",
"### Quick plots\n",
"\n",
"Common figures such as scatter plots, histograms and barcharts can be generated and manipulated very simply."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"from PIL import Image\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# As data for our plots, we will use the pixel values of the image\n",
"# Open image, convert to an array\n",
"im = Image.open('python.jpg')\n",
"im = im.resize((400,300))\n",
"arr = np.array(im, dtype='float')\n",
"\n",
"# Split the RGB layers and flatten them\n",
"R,G,B = np.dsplit(arr,3)\n",
"R = R.flatten()\n",
"G = G.flatten()\n",
"B = B.flatten()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# QUICKPLOT 1: Correlation of luminances in the image\n",
"\n",
"# This works if you want to be very quick:\n",
"# (xb means blue crosses, .g are green dots) \n",
"plt.plot(R, B, 'xb')\n",
"plt.plot(R, G, '.g')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 64,
"text": [
"[<matplotlib.lines.Line2D at 0x3558690>]"
]
},
{
"output_type": "display_data",
"png": 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YTq3xlGcVp+LMqegP4Sdku6Wyw70ojbI9+LP0KM9e0Slo9eRBMmKgYX3511+X\nK4CVK0XQs7PlCuD660XYv/wSxo71PN+PnrZQHmwlKjSMddfJqsvElUnUBhdQ6VKTre1GK4PQuBxC\nXop0kBq2o3UhD+DJWVBi360ose95eEf2ubnQvz9ERnoi+4ULYfduEXuAcePgySflNjIS/u//4NK/\nWRgw2srZZ4WxIGIdjxfMrp9wDTGG4HK5PHXkFR3DW+hdwOFp8Mbrcj+Ahbwt+LP0qAlaBdB8o4/X\nX5d/3jSebPUuNvbkk7BqlYh6WZlE8AsXitc+cSJMnSr777kH7Haxeh58EA4cgJB4KwdrdrL1u628\nVmEhL1smXA3OCKod1dhdSug7hcYRvR6ojhZxV/nwihZQYt+LaK7Rx/bt8s9mE6vlqadE2L0nUe+8\nE/r189gzP/wAO3fKY956SyZdt2yBr78Wwf/iC3lseLgI/223yfExSSLug9ypPH7FCwzp358QZ390\nRnvH35wfR2OdTv2EbN2t6hSlaAPKxullaL6698RqSQn88pdw2WXw8MNw1VXw/POyf9o0GDQI3n67\naU35xj1hva0egIoK2KKzUBtlJSz+e0YOjOdgYTbbr9/NumeG8NLAMKqcVc0PtCXfWXF6Cs+BmGyx\nboZtA0cI5I2H9etVRN8G/Fl6lGevqMdmE//8X/+SCdMDB0T8/+//4Lzz4JxzRKwHDICDByE6GjZt\nkgyZe++VH4CXXoJRo8Tq6dNHFjytXAl//jPs2yfH//tfuOMOCL4rjZqBDRdCDauew9Jx6cz/1oQL\nlVnTaTgNcPgKePN1EfVb0zyVKMGvmn77En+WHiX2inq06Lu8HPbuhZQUOH4cYmPBZJJofPt2OQ4S\nuU+eDA89BDNnQlWVWDf33gvHjol/f/fdkJoqE7faY2pqIC8PPh81ncr4rdLdSO8Gt45gYwg6l4lq\nV6mK2juD48MhuBLWfCSLpDS0FEpoWApB0Sr+LD1d2rxE4T94p0babCLKtbXix3/xBWzeLBOwtXW1\nxLSCpB9/DPv3w8mTEtm/9ZZYO9HRMH8+PPaYCP7YsZIz//HHcGKiBeNFVmrDPwGXCXR1T6pzU+Os\nAqqU0HcWp86VPrDezLRAcCmUxULueNjwqhL6NmAw+HoEvkOJfQ/gTOrMtPYclZUyuZqTA889JzVp\nZs4Uz33xYong+/aVCD8hAb7/XsTbaITp0yE9HTZskHMvuACKiuDii2UCduxY2N3fwhcxVqKvCaPY\nVgrhWfJD7hOtAAAgAElEQVTiapq/63DpPH1gvelrhcF1n78j/MyEPoC7TjkDuJmZ+pr2AJrLomlc\nnqAxjdMsJ02SH4crrxSBTkuDn/1MbocNkwnZYcPg1CmxdsaPh6gosWL274dZs+CuuyS6HzwYPvsM\nfjjPwo6ENLhxOt8ds6HrZ6V6wE4Ko7aiG/xRwwE1d2Xpx5fLPqM60rPtBtbsal6MayXzqV2ZOAFc\nLiEx0dcj8B1K7HsAZrNkzTzwgGTAeLfna4nGPxAaO3aIVTN3roh2eLgIe2Ii5OdL6mRWFnwQbaHq\n+jRq504nKtZGSIj8GGzfLvnzSUngNFtl8nXEViovt6Dr8728iJuGzUNayqhRNk7bcQPfT5Her9ap\nslCqJgJumA3RzVSP60gzkY78UPg5jb8vgYQSex+jRejeXZpGjWrYaBuaLo7SFjvdeKPnB2LlSonQ\nb7tNbJh334Xhw+X84GC4/36xdJKSIN9upTZehLz0Ugv79kmEbzBA4QQL2VPSoN838uDcVAzuUFym\nUrnfWMSVqHccHdDvkEzArtsG9mgIKYeIk3B7M3WhO7J4KoC7TlW1kAkcCCix9zFahJ6dLXnxX34J\nzzwji5RaWhyl7XvkEVi2zNPGr6REHjtjBhw6JJOsixfD4cOSPfPqq5I2uX8/9dGdLi+VO+Je4MAB\nKYDmdIK7j5WquJ0iNG6g7wGco/8JIXVir+yZzscN2BI9JYcdJrm1h0lT8M5k6kKIOA7XzQ+4Esda\n8BOIqNTLHoBWCvjVV8VbX7hQUiGh4eIokEh+2TL4wx/k9rnnxGtfsAAiIqQo2RdfiKDv2yeWDIDp\nJxb0/a1UlcqknNMJ7hkWjITiGLIdjNWQdwG6N9bj/sl8T0qfomtwA4VDIe5Iwyuj2iB4yirbt08W\nofdOt+wMfjsIIutaix6Y3TTTpxeTkCAJDP6Kqo3j53z9tQj9eedJhD5kiFgyF1/csPm22SwCf955\n8KtfyQ/E/PnyAzFvHnzzjVymXnyxPG95uYi93Q72SCtlfXfiGCqTcu4qM7o303FEZMsXP7QYhu3A\nPcMil/nqt7hr0QGx2U1tFJNdRL5kCPw9p/OFHsBQ7dkOsKArNtbXI/AdSux7AOXl8MQTDRtta/nu\n3vtsNhH2L7+En/8cHn1UPPohQ8BqlTz6p56SlMvcXMmvNxrln6NKbBt9QSqODS8QdJ0F928GQUKW\n10D6QPJbcH+Mjz6JXoy70S2A3gWORtnPTkPn2zaNyb9AbvNSYNPLXftaPYjBgyVbLVBRNo6P0HLr\nwVNx8sEHpYxwRgZ8+ils3SpCbrNJdo3DIZ77kCHSHeq886QK5fbtUgbBYIDQUEmbDAkRsS8tlX+E\n2OCu86FsEEZnFI5+n0NkoWdAbsBlVI1DuoqyaAi2w5v/hBvmeKybgzNg0GcQWSA+/QufwfExXTuW\nAK1nbzBIOvKzz/p6JO1HlUvwQ7Rc+ksvlcJkJSUe3/53vxNb5n/9LFhPWQkzhTG9ah1vrTNz9tny\no/DQQxL1v/eeRCw//CBCr2UbBAfLZKvjaq8FNMGlXgtxgqAzqlEGOm0t5PZdGrzyvmzfPFUajGgl\nDiAgxbe70evBYglcsVcraH2Ed259RQV89JHHtz9yRI7/fY2VfWVS6Coi2cJbb6Xzk5/AFVfIitb8\nfFi0SPz9oCCP0EdGSulhAMatAUPdssGKvnLr1IOhTui9xUpVoDxz2vp5xVo92+vXNxX3GrPk1Afg\nqtbuYMwYsUu1+k6BiIrsfYxWRnjuXE9nqLvukomkgiumsyN7Kymxqbz9k2289KyZsDDJlwexeZYu\nlcyboiLZFxEB5VPqovmYwxCV07T+OShR706cenjicOuTrd4VLFX1yk4nOhqmTIGRI2HFCl+Ppv2o\nyN5Psdk8ufU33CALoUCE/rPP4PU/reNPQRYi3n+Bhz4z8+tfw09/6ukTe+utEtFrHaNqplkoH7kF\nwk409d5V1N41nO5zdRjhhc9Pn1UTwKtau4OqKll7csMNvh6J71Bi30WcrriZ5tkvXSr7tm4VL15r\nFvLee/Dbu80kJ6eTXwrHjkv9+OPHpaDZpwMtFNaKF99v5zp0JWZq+lo9+dPQUIiU0HcNjT/XqnDI\nnQROEziNku1SbT598bGKWKjoL+0FIaCLlXUmWjCk04ldWlnp6xH5DiX2XYS2MlYT83PPlRWv2uKo\nd98VywU84h8UJHVt7r5bVsJmZko9+eHDJaJfvhxGj4b334eKOdb6y/6TNRa57NeiQw0l8N1HVTRk\nT2pYanimxePDe0+O3zNSOktVxIL5qOd4+AmZuL3GIitcNVvnGouyddqJ3S72qMsFP/qRyrNXdBLe\nlSi1Cdjf/U4i9WuukZWxZrOc88EH0ipQK5XwwAOyGOqLL0ToH3pI1rtcdBEUF8sPxZIl0l2qshKM\n7mYu+99cJysiVQXK7qMoEfZfC6uOwmsZDSNw7+qSMXVF5GoiRNRHbIUR/2l6XPv/VLZOp1FWJgkQ\nen3rlWR7O2qCthPxtmY0UfduEfj88+LLf/SR5MdfdZU87vrrpe78W2+JwN9yi9g4U6ZImtinn0rO\n/MmTdROw5cDsBSISpgrQ1zUOqeoLuCGisGFUr33UKtI/M9oyz+HSwyOnGoq8ZsH0+0bqC+WmQvp6\nmHpf/UplclPFstG2teNahk6A5sJ3NqGhsqjw6qslYPr667b3iOiJdGme/e23305GRgaxsbHs27cP\ngKKiIq6//nqys7NJTEwkPT0dc505vWzZMtasWYPBYOCJJ55g2rRpnTrgno4m+PfdB3/9q+zT6tvc\ndZekVk6ZIvbMc895qlOmp0Nystg9eXniMQ4cCLt2yR9rfT2PmRYMsVacffdBWJHP3qfCi8bZM96Z\nNSUJUrZYE2xvEQcl6F2IySTZN336yPdywgRJU26tdHhPp0vFfteuXURERHDLLbfUi/3ChQvp168f\nCxcuZMWKFRQXF7N8+XL279/P/Pnz+eSTT8jNzeXKK6/EarWi1zd0i3qz2IMnnfKmm8SDN5vFqrns\nMnjlFYnwP/sMXnxRCpiVl8sl5sSJ4tt/+ql0kYqMFL/+qyEW3H2suGoaeb8gk4Bq1WvX4ALKB0LY\nSTDWNr8mITe1aalgrTdsc8cU3UJYmGTgnHeeXF2H1bliERGBG9mfdoL2kksu4ejRow32bdq0iZ07\nJXJZsGABaWlpLF++nI0bNzJv3jxMJhOJiYkMHz6cPXv2MHHixHYNzh/R0ilXr5b68yUlcO9/LWz/\n3MrZi8L408PruOmnZux2yarR7JkZM2BHUhIVugJcw6uhIIUKewx7y/pjPHcjTmOJvIA9VG5dSPiv\nd7TNblCpl63T2OpyA8cuhKIk6PstDPgS1u6AebMh/KScV5LQvJi/uU5F7D5AsziDg6U0wrnnyhqU\nEydkvizQaVc2TmFhIXFxcQDExcVRWCg1VvLy8hoIe0JCArktLFlbvHhx/XZaWhppaWntGUqPwLvO\njebZv/uuFCm75hqovdFKXtBO8gqh/1ALmZlyyT9ihOT+jhkD334LZSlHPCUMztqNCzDW9MehCX1l\nDJwaCWd9XDe1fga/8EroW6fxHIcOGPwx9D0sE6oAE1dBXurpo3atsYiiW+nfH84+W+pGnXuu9Fu+\n+WbYu9fXI2s/mZmZZGZmdspzdTj1UqfTodO1rCQtHfMWe39HS7O89FJPauX27XK7bh1csSYMzBBy\nKpVJRS9Q4xKPftgwieg//LCuho2htsHz6vJScVRHw9k7pM75qZGerA0NFbF3Pi4dGNwygXo8GYbu\nbJgVo6L2Hsell8q81tGjktX20UfyvfL37JvGgfCSJUva/VztSr2Mi4ujoKAAgPz8fGLrklfj4+PJ\n8eoMcOzYMeLj49s9OH9BS7P84APJqPnd72QiaOVKmYR9+nJpA7f9lm3MnGomKwtSU+VSc9kyycqx\nR1pBVxep10QSmTsb9/FRYKyCsgFQEy0RvdY9ClQ6ZUdo6bNzAbVRsh1SInnw3oudOtIOcKZFJm9v\nnB5wHaK6Er1evnvHj0NMjNiiW7fKlXXdNKOCdor9rFmzWLt2LQBr165l9uzZ9ftfe+017HY7R44c\n4dChQ0yYMKHzRtuD0XrI3nGHtADU9s2bB3NnmfnP7em8/JyZp56SS8tPPhFf//e/lwUfrhqZQTLW\nxhC9bh9lL74NyetlMjayoKE4qEi+YzR3NVQVJRG9HhF5kGi+LKHhYqeO4J1339HnUtQzeLDcVlTI\nNNaiRbKAKjNTvo+B3GTcm9OK/bx587j44os5ePAgZ511Fi+99BKLFi1i+/btjBw5kvfee49FixYB\nkJyczNy5c0lOTubqq6/mmWeeadXi6U1oE7NHjsgE0e9+Jytnr71W0ie3bJFLysREycb5z3/gF7+Q\nlbFvvQXO9HVcFDWH644dxnGyro6Kt63TyOIBRLAC4+PtXFxeH1ptCHw7HVY16hpVMkg8+Zq6KF+z\ncToSnXfmQil1lQDI1fHJk5LODCL2l18u22PGwLZt8j1UqEVVnUJzi6mS7rNQ6LAy8YIwtt4ptU3+\n7//g2DG47jp4cI+F2FFWDn4dRvKBdZxMWcjJPpvRB9lx/HC+lMG9ry8YXK378sqzPzPcSBPv4Loi\nKSUD4O919YS0OvM1EXBsovwfQEOPviPVKTtzoZSqkgmI2IeFQVQUvPaaRPLx8RJA+XM+fUuoHrQ+\nJivLI/QaNRFyyb771FZue0su2QsKJHf+mmsg4TwrB2vkkn7/Nf04kbAGV3gBDlMRDNtByA23Qm24\nPFljMVelis8c79TKoDqht4fBmt2ec9avF38+uNxj2zT26LXovCZSVsOeSVTdEb+/MaqcAuHhMGqU\nXC3/5S+S5rxtmyymevddX4+u56Ei+04kI0NSvu6+G364ZDr7qrfStzqVUZ9uI3GAmcsvl3IIDz8M\n2ZOnS/PvFtB9Oxt3wocyIQuqyUhHaPx5/XARROfAD5OlSqh3ZcnTLYgKsUkhMy0d01dRdQCWU9Dr\nZX4rNFSy2Nxu+b7de2/L1WV7G6otYQ/BZpNVs8uWwSNP2Hg3yMKEwhfIeNPMJZfA8Qst1EZZ+eH7\nMAZ9/ix5Px2Bw+3lxWui5NKBPQIMNQ1bByqB7zjVEfB4TsuWTFtEVK2Q9RmRkbIyduJE2Ly5d1o1\nraHEvgeh+fejRsFjj8GpU3DJJXD4MBy/eRCn7OIPX2Sezf8+q7MLFN1DlRme+8LTSKS9oh2AUbWv\n0aL6mBhJW7ZapeT39df7emTdixL7HoZWG0drRAKyiGruZ33E5wUM1mt54oqX+dW3o3CHFzR8gjOx\naZSl46Glz8IeCoenNKw1D20TbdVExKeEhkokbzCI0BcVQVKSZLP5ewXL9qDEvgfhHdmvWgWFE6SI\nWXlxGGPOr2Rf2U50VX2IDDdRardhNLrEymlOpJSQtw/tcys8B0LLYPVHcOnS9om2ynrxKUYjxMVB\nYSE4HFIY0GSSRVNDTtPpsTeisnG6Ge8mJRo2mxQ+e+ABKbr0VLaFsuvSqD77Dcr7SdbNgbyjBNX2\nJyzcRamrEIw1OGhB6EEJfXvRIXXm170Lj+WKbdPeBU0q68VnhIdLZP+zn8GgQbLvggvke/bSS74d\nmz+ixL4daLVwNMHXonmQFMyvvwZDrJXC0J3UGsW20bmNhLj6YTedoMLVTLqe09BNo/dT2hLMeJ+j\nd0kzEI32ivabUupCTcR2Pdr6y6Agz76774bFi2VC9ssvxcZZtUoycBRnhrJx2ol3k5K//a1pnv2w\nP03nsKFRaqVbBzo3ofTB6QBHrQGXPQhDSA1OU5EIlOoq1TKaPdNSGmqtEUx1tf3zUuCf7zXfNMSf\nRLsXzxlofrw3UVFSpviRR6T5T2WldHUbMEBqTYGsTp86NfAmZ0F59j5jzRpZsXfkiCzsALnEBDhZ\nbuNXP8R6WgZ6iVKQoy9246m6/TpPATRFxykZJNH7ppd7hzD28jmDoCBpCm4wSGRvMsGjj3rq2+Tn\nw/nnS3SvrVKH3ptHfzqUZ+8DbDYpo3rTTRKB2Gzyb/t2+POnFv7tno3J25mpE/qR4anU5qR47a/7\njwtUve/I+2782NxUePYbqIqFG2b3jroxvXjOICgIQkJE2CMiICUFfvxj+OUvRdR/8hPYtEmieK2y\nbFaWbAei0HcUJfbtQFs89eCD0nbwwxgLScvTGPan6YybaMMQayUrdye17lqZKATCS1MwVQzGeqDO\nkGwsVC1l4/R22mpXOYxQafYUMXOYoKqu7LDTABV9PGWIe1N1yV40Z2D06p4RESERfVKSfIcyMuCc\nc6Sr1JEjcOCAlP72FnYl8h1D2TjtQCuLcPUzFvqMsPL1ia8osctE7NDKOQwZUU5mbt1ineJh6EZt\nwF0TDDp9fZ69Sqs8QypiILzus6sNgae+le3bJ0NpvNT6BxHG4HLPYqkTyVKTvhd63v6Goe5KNzxc\nigEWFnqqv152mVwVa1584+KCCkF59j5i8j/SyMrdWX8/2NGPMYPOIS87FGd1BEVrXkJ/TzI1QfkN\nH+gwiZevR4l+W6iOlFLD0V4tLouGQulgEXF9raxE1lbCgmcy9obZXet59+IJ1M5AW/mq00F0tJQi\nXrIEfv1rKQx4//2y/fe/i4UTKDVu2ovy7LsZLc8+1Fjnp+alMKTqWuJDzuHT41nkhe6gMCiLEYvm\nU+OobPoExlrPJ6+E/vTUhENJoud+ZQyUD/JYNTXhDa0O7+qSXe159ybLqJOJjBShB+m3/Nxz0KeP\ndJUqKJCsmgULJKXyscekaqU3yrbpXJTYnwGayGt59tc61jG4bA4rRr9H8NsbKMiua3ThMkD4CfbX\nbkUXWu7bQfsbTgNYp0KtSe7bQ6UMsdZEpDIGnt/bsKnIppdbLh3c1Z53L55AbQ9BQRLN/+IX0jkK\n5L7JJB78ypUSrW/dCrffLseHDJHmPmqhVNeibJwzwNtHLCmBq6+WbjiRkTJZe9njC8iOXAcGyfU2\n2GNw4cAdVObjkfdgnHpp0AIy+bpmFxybBNHZ4sev+VBWwDbOk+8pefM9ZRw9gPBwOOssGDYMPv9c\nJmTdbklLNhpl/8qVyoPvCMqz70ZsNrjxRhH6XbukwNmRI/Dss/AvQxr5weIP6zFhsl5HzdlvgNHh\n41H3IJpbGOUI8pRy1nx15YX3eDQ/fsAAEfXCQrj1Vqny+uijsH+/fDfMZrFsLr5Y6twoa6b9KM++\nGzGbRejvuQc+HWjhwmfTuOS56XzzvY0TeXJJH+KOwb3qELUh+UroW0ox1W4royH7Utn2tkKUF94j\nMZk82263NPsODZVUyhUrJHUS4KKLROj/9je5v3KlEnpfoyL7M8Cy2cKmbzdzqsSO8fj51Ji/wh1+\nHABdZSzhr+6h+pL7iA4P5ZQzG/p9I52mVMaNh8afxXfT4I3Xm1ohqkFIj8RolGhe6xi1aBH8+c8w\nebI0EykpkbabW7aIF69SKDsXZeN0ARkZMhHr/QeatDxN+sZqNCp1kOScw8ln0zlpCYOguqIfSuib\n4v2Z7L8W0jc0PUd54T0CzarRatZo2TUg0XtOjjT6fuklyZGPiJA1KN615lUKZeehbJwuoLnKlifz\nwxqe5CX0w8JSqF7/ArZLLGDyqu4UyELf3N+kG8i5ULbzUiSTpjk6szm3ot2YTDLpWlsrlg2IoKem\nShe2+++X78bKlZJSOWmSRPTewq5SKHsGKrL3onE0b7PJxFJEBLxjskD//Zx0H6K83E2N8QS6qr64\nXW4IKZGJRGcwmMohqNq3b6Sraa4yZ3NXMDmpMGCvpxIlSMeo9Ld6TtSuJoKbYDJJzRqXS6pSulxS\njOz77+Hyy+X4t99KqYPISE+GjYrgux4V2XcSjaN5kPodq1aBzWDlYFUWp6qPM9R4MXw9h+CyJAgv\nAoMTQsog/CSYernQg4i6d4lhaP4KpmwQPPUd1AbL/bwUEfqeFLWrieB6tMlXh0MqTl57rQh9UBDk\n5cl34/rr4fnnJbL/0Y/EusnKksepCL5no8TeC7MZLr1UovmjR+U2KEhW+DmqxMIZGZ7KwaL96JI3\nUt2/7q+8512kdA9u4NgFzR+rCYfgCqiJhkcLJKXSu758T0EtiqrH4RCPfvhwqVPz8ccwezacfbZU\npDx61FOcbOVKiI0V8VcC7x8osfciI0M64tTUSLPwmhqp23HnnXD/8HUMLJqD9cFtEPM9br29aRph\nb5+M1X7UiuPBBeSdB/YYTyVKgLI4qOgrQj9sh0TLPSmSb0wvqirZUdxuie4PH5bbCy6Axx+XK95B\ng6Tdpori/Rfl2XuhefR2O+wItVAbZaWqNIx3Lev44+/MfNTXQv8kK7nGnb1b1JvD+4esdBAUjfAU\nGHPpQF/3/7n/WjDZVdqkH2A0SvpkWd0C7+BgCXAuvFCi+hUrIDtbNQzpSXREO42nP6X3o03Mvvuu\nCP2eOAul0elUOEsgAqZuGknIuePROUvJNWX5erjdQ0tXKS49vPIOTL3fs08Tei275ke/hor+nvry\nCp+idYMyGsWqCQ+X+0FBMgEbHw+5ueB0SmkDq1VWhs+dKxamlrCghN6/6ZCNk5iYyNixY0lJSWHC\nhAkAFBUVMXXqVEaOHMm0adOwec929lAmTZISCCdPyhcgaKBVhB7ApaNKf4LiflupiQ0Qj745odfu\n611ww7UQXAq1dY1Y8lLgwGyPJ2/OhvATHhtH4VOCg6W8sMMhtwaD7KuokDmqoCBZ3TpwIJSWwrp1\n8POfS7GyG2+U6F7h/3RI7HU6HZmZmezdu5c9e/YAsHz5cqZOnYrVauWKK65g+fLlnTLQriQrC5Yv\nlybHkyfD13vrJu2qYtDpvVSvsUffW2mcaeNNbqqUFx6cJXZNSYKI/Otve+waNenpc4KDoV8/2Xa7\nJatm7FhZHFVaKgukzjoLPvtMFkcVFkJCgnSL+vvfITMTdu+WlbB3390wQ03hn3R4graxf7Rp0yYW\nLFgAwIIFC9iwoZnVkT5CK1HsfT87W/7wn3tOMgxuuw1+qlsHRUOJtifjdrtafkJ/oqWrkZb2O4xS\nqtn7POtU8eC18sI1EXAyqelj1aSnT0lOljz5khKpylpeLr78v/4F48dLJH/VVWLbXHihfA9mzPAU\n+bv9dnnc4sWyQOqVVzwTswr/pUMTtGeffTbR0dEYDAbuuusufvaznxETE0NxsbSPc7vd9OnTp/5+\n/Yv6aIJWq9NRcbmFo2VWdI4wCp9Zx7j7FvLfnM0cLytCp5NIqL4Kozf+nG3T3Ngb73MD2ZdAZR+w\nR8M5myDU1rD0MEgpg3tGilUDXdMBStEudDoYNQoOHZLbr76SBVElJXIsNFQqtB47Bm+/LVZNaamI\n++bNcnWr6tj0XHw2QZuVlcXAgQM5ceIEU6dOJSmpYZSn0+nQ6ZpXx8WLF9dvp6WlkZaW1pGhnBZt\nEnbpUhj9Nyt5QZJJkjLHwvtfHed4SAEYW7Hj/VnoofmxN+fLlw8Q4b41TYQe4OC1HqEHidbzxnsy\nbpRV0yMwGCRa379fIvhjx6Tc8MiRkif//PMwbhyMHi3fhYoK+OEH+N//4LzzxM9fulRl3fQkMjMz\nyczM7JTn6rTUyyVLlhAREcGLL75IZmYmAwYMID8/nylTpvDtt982fFEfRPbe1fd+/MZ0MnO3Enwy\nlWtLt7HvnPkcqN3arePpdlr6sfLeX9EXTiWJTdO4r+vUhQ3LCkDPKXkQwGiZNiALosLCxJ+32+Hh\nh+FnP5NjDzwgefK7d0v5j0mTJM0Y4I9/hL/+VbZVc5GejU/KJVRWVlJWl6BbUVHBtm3bGDNmDLNm\nzWLt2rUArF27ltmzZ7f3JToVs1mEfuJSC998V0pI7QBSDq3n46iFWEs+B6cJXVXf3pFp09x7ODwF\nyvp57ldFQU0I5I6H8jj4droI/eAsidjtEQ1998ZlBXryQqleTOMLZbtdInoQi6a8XER94kSpRFlS\n4vnb//prz4rXd9+Vx6xcKemWK1fKfW2/ovfRbhunsLCQH//4xwA4HA5uvPFGpk2bxvjx45k7dy6r\nV68mMTGR9HTfe7mahQNS4+ZEqMw2fXPxRCqPDcPZrxAAd+ip3iH2jSP4vBRY/5Zsz7pVFGPjS3DD\nbM/CKEc41NS9+dxUOe4t5CrDpkeg10PfvhKdnzwpfrteL577gQPwn//AU0/JpOp//yuCv3hx0xWv\nERENo3itBIKaiO29BMQKWs3CMZlgfdB08sI9lk2QfQD2oAK54+++fGOcOqjsC//4VPq4Nq7weN38\nhitdoWVrRtWX9ynR0VKLJjwcKislhfLYMfHm9+yRBVPp6bBsGTz9tJpo7a2oqpenwWyWOh9bt8KE\nnHWEOgYAEHQiFXt5uOfE3iT0AAY3RJ6EH90r9xtbMY1TJFuzZpRt023om/lWRkXJJOsFF0h6pNUq\nXvu//iUCHxEBe/fCzTeLXaNNtCoUGgFTLiE8XCKjDRULCXa7wa3HHnFIItbejhYJaFZMTQSE1qXD\nqpTJHofLJYuizj5bsmUGDZJyBuHhUlJ440b48ENZGwKSF5+aCnfcAdu2qfIGiubx68i+8SIpkPsZ\nGU3P3Wq08FnaQEh5iRpTIehcklro79G8u4VtZ92snXc3qDfXSc2a4HJVyqAHERwM/+//iVUzYIDc\nHz1a0iiffFJsmkcegX37ZDHUunXi0S9dKvbk0aMi/N5Cr1A0xq89+8bNjJtrbmzZbGHTwS0Uluf7\nn7C7aP7nWJtbcOnAHg4h5bL/+ymyGMrthndXwdT7mnrsqpF3j2P0aPjmGymrPWkS3HUX3HKLlO54\n4omGndMa58AfPSqPO3JEsmoUvZuAbjiuCfx998Hf/tZ0UqrPw4Mors3vlNfqdpyAK0hq0LREWSxE\nHpcIvi3NQdREa48hJERSJ0NCRKjPP1/+js87T8TbbG59gdPp/vYVvY+AFntoGt1kZMD/+z6J45UF\nlA/r6ecAABJ8SURBVNSU4rf5lCVxEHEKDHU9XLWIvjpS2iDmpkL6+uYjeEWPIzhYrBmHw7PaVePL\nL8XGueYaePVVWe3amni35apW0fsIaLG32eDiZRZO9t1MaYUdfVA1Jr2R0ppS/7NtGuMwgLFOEbzT\nQg/OkMlWJfB+hZY2OWaMCP+XX4rwX3ml1JUPCYEXX5RsGq0fckvira0d8T6mGn73fgJW7DWhzzWn\nU2ov6YSR9VCcRk90XxkDTxxWIu9nRETI6tann4YPPoBdu6TiZHQ0FBXB55+LeD/3XOsevSKwCdg8\n+6ws6DPC6n9C3/j/qqX/u4JkaQpyNE3uV8bA83ulTs2taTLZGgipoz2YyEhPCQOdTmrCh4Y2jLgn\nTpR0ykWLpDb85MlSi6ZPH/jzn+UxH34IP/5xw9x41edV0Zn4dZ79RpeFA0X7fD2MM6e5apO1IWCq\nhsoocIZC7gVQ3R/MR8FpEtHXShhoi6NAJltVrrxPiIqSaD0+XlazhobKbUoK5OTIOaNHS/GxadMk\nev/yS1kUtWULTJnimWsaMkT+KRRdhV9G9pbNFtJeTmPtl2spqiry9XA8tPXqKn8M2EM89wvGwlPf\nymrWJ7Lh0QJ4LUOEPnGn5MQ7TaoTlI9pXISsokLKFOTlSamCgQNhxAg4fFjq1qxfL7VrrrpKGnjP\nnCndorZsAYtFovsjRySTRnWCUnQ1PVrsNVGf/up0bNXybcjIgE0Ht7Azeyd2Zyspib7gdE2tSmIl\nQi9MkZRKjaKhUrumcTmClkRddYLyGSaTZ9vplH+vvw5r10rhsbg4KXcwYwb86U/y9+pyyWTr1q0i\n6tHR0hJw6lTJHtMWRynBV3QlPXaCNiMDluWnkZUrdsWc5Dm8MDWdd9+FG7/qgzOouNXH+wRHUPMd\nrqBhHvytaR4bprUJV5UT71O09MjQUKiulrVqKSnSyrKo7oJy1izZd29d+aG5c+GGGyTzpqJCerwu\nXOjJsNE8eZVJo2gPvTIbx2aD8x6Zzg/BW0kdlMr6Wdu4+rGF6PpZOVi5Gyc13TTaOk5XEdNphJcy\n4fZLQO/2nF9lhuyLYcOrHsHWVrFqE64lyqztaZjNUgt+1iyxZYqLxZIpLZXjgwZBba0sitq1S9Ip\nVTqkoqvplWIPkF1oY/gzg8FQjdPlBp0Td09ZINWc+H89B9xGSHoT8iZI56dNL6tywT0QvV7slcb3\n+/SR7JiDB+GyyyQiv/BCqUvjdIo/73TK4qe//EUyaKqrRdC7Y4JV/aAENr029fLrT83o0eNw1+LW\nOXqO0AM4TA3vV8aIeEfmSXmDIR+KL6/KBfdI9HpIThYPPjhYhH7cOFnYdNFFcPHFEtXPnCmNuc89\nV865/HLIzBSLZtUqOfbTn0qTkO5AW2yl+fvaylmtOY9C0RI9Vux/9LSF//dlGnZXpWdnd2t9gyqS\nXh+VC2nbp1FlFjum2qwyZXo4kZEi9DExsnJ10ybx18eNk2ybd96RypMvvSSlhB0OWL1a7JtLLpGK\nk5MmSfbN1KlSgvjxx6UbVHegtRjUql2qEgmKttIj8+x/9LSFHSfX4sQuI9REtzvLH3jbNCWD4NV3\n4KarIPQEmBwQXiz78yY0bOH35jpl0fQgEhIk9x3EVy8pEb89NlYKjm3eLPnvu3fLOc89J+IJMHiw\n3F5+OXz0UcPnNZuln6svMJul+JmWo6+EXtEWepRn/6OnLewvtJJn8MEErDfeQu8wwcrjTSdXy/tB\n0TlQEyUCr4Td5+j1kvduNosdExcHJ05IFO9ySb77qVNQUyMLmn74QbJomvO/QSJ4m62hqPYEb1xV\nuwxc/H6CVpt0uuYNT6plt6EJu3br1ENNJISVSAOQ5/fC8TGe87XJ1ag8GFynCl/PUatYuxmdztOA\nKyREJknj4qS70//+JytVX31VWvW9+KJn8tRmg3fflVo1pxPtniiqqtplYOO3Yq+J/L3/tbD9cytl\nId9Q5jop0XJwadc3AHcDhy+Fobsg93yIzofVddfrt0+GNR+2nBapmoD4BL0egoJE3AH69RPh1iL6\n3Fz4wx/Ee3/6aVnApEXjZ5K10lNFVWXjBDZ+KfbFxfKyDzwAX6Z4RfQu6talu7veoy8dCEUjPQuc\nziRCV+mTnYbJJDnrjTEaZYJUIzQUhg0T+6W0VHx3vV7+hnbtkm5PtbVScOzyyyU9cssWierPVKyV\nqCp6In6ZevnAA3K7cCF89ZlXI2w9oOsGobeHwur/tT97RqVPdhqa0Osb/TX26SP1ZoKDpejY4sXw\nxz9KR6dbb5UCZH/8Ixw4IJkygwZJH9bYWBH4LVvgl79sX9bKjBlNz1VVKBX+jM8i+6NH3fz4x3Do\nqiQqgg7jxiG2iqETh+PSSQGx7ItBr4OE/0FeKsR8D2s+EotGReg+IThYJko1IiKkgYfTCcOHy8Rq\nSYlE6YcPw7ffwq9/LeUHGlsrd93laeXXuA+r6tGq6E34pY0zZYqbAweg4FYzhHSwHn1zjbkLR8NL\nH7ZfwGdapJRwbZjKtukAQUEykept0wQFQf/+kiVTUCDHSur+BBYtgvffF8smOFgmU7/8UtIgH3+8\nafZMdrbUiH/qqaaTqF05wapsHoUv8Esb5/335YveZCVqW3A32q7u0/ScsBPtHFkdWs34EVsl8lfU\nYzrNf5leL5F6UF1hz9paqeseEyP3g4KkeFi/fiLEERFSGjgsTMoSnHsu3HKL/CCsWCErWUtKROi9\nm3vYbPDII/DKK02rR3p79F1RWVKtZFX4Gz6L7M85x83BK5Og38G6nbSefaMdcxggfxyc9ZmkSRrq\nCpyUDoDQYjB5eQPahGt7ovQAy7bxTmVsjPdEqTaZqrXZa4mQEFmZWlYGV18tq1GHDZOFTYWFkiVT\nXi7ifvnl4s/fdpusar3sMhF6nU5+GG67De65R7z6lSs90XRr0TV0feTdE1MzFb0bv7RxJk1yk3XJ\nGVo4JQnwbF1nqmssIu7DdoggnxgFfb6HgZ9IbRpvkfYuKdzWjBsfe/nehboaF+3qCkJCpJRAdbU0\nxY6JEf+82KuS9KhRMhkaHy857cXF8iOgdWWKjJSywFq0e+mlUvJ340YpQaD9pV14ITz/PFxxhUyq\n3n+/x1PPypIfhwcflGJkdrs8TqcTkfbVqtWWUHMCiu6kx9k477zzDklJSYwYMYIVK1Y0e87upDQw\nVjZ7rAHa+8pNFaGvNnsyYdav9zTxMGfLIieTXX4UvKPx9mTcdCjbJrNJZgk07XQEkmXS+FyDQcS9\nf3+JbE2mhucYDA3PDw6W24i6cj0hIQ2Pe9su0dGe7WeeEdEGEfmEBBg/Hr7/Ht57T8YwaJA8X0wM\nWK0wdmwmlZVSs33SJCkYds458hxut/wg3HSTVIP87jspQ3DHHfDEE/Dkk/IZ3HOPLHi66CLx3L27\nNU2aJP1YFy6UjBu3W2yav/yle4Q+MzOzzefabDJuf+o2dSbvzx/p7e+vI3S62DudTn71q1/xzjvv\nsH//fv79739z4MCBpuedtROMtU39d+3Wjlgzz37Vclcmb0H2FnTtR0Gjizs76XSNBTuz2Ujc7RZL\nRKNv34Zir9dDaqpE1Hq9ZKQMHSpi3bevnBMcLMcHDpT7JpNE15qtMmyYCPfo0RJpR0V5LJi//EW8\n77Awef777hPL5PrrxTP/7jup5BgdLbeTJ0tJ35//XJ5bOjNlsnmzPFdxsbz+5Mniret0Inx//Sus\nWSM/AseOSZkCzd4ICpLCYk88Adu3N/XUs7Ml6t+1S9Ipa2u7V0zbKhZdPSfQVfR2Mezt768jdLrY\n79mzh+HDh5OYmIjJZOKGG25g48aNzZ/sMng8ejeQ9UvJf3/2K3jYDY/lS6mCtkTYrQl6G6J0bRWm\nN0FBTfdBw0h58GARcZfLI9rekXdoqETMer1sa8JrNMpjCgpkn9EokexXX4lIulyeuupPPSXCf+ON\n4m2DiPZDD3la4yUkSB66zSYTmVVVYonExcn4zjkHXngB/vMfz2sbjfIen3sO9uyBa6+F3/5WMmAu\nvliE9ssvpVjYtGkSmVdXi4AvXy5jCg4WH330aHj4YYnsd+8Wcf//7Z1fSFN9GMe/q8YrukqhXLYZ\ng+XKP/PsgLRugiLtD8Iy7KUCRXAF/bsIIqKb2E1WUhe2hCAK5e0iL+zfhRN7IdrwIqEmUoIZTdA1\nhawX/FMcW8978XNHZ6405+Y5/j4gc+ccz/l9edz3/Pb8nj17+JDdKNrbp8zx+nWgupp1jJxOejqb\nzZ8+zR4bGthNqKuL3USWmpm2t0fn6CPdKKcvIHM4S4m4m30wGER2drb83Gg0IhgM/nScaexv1hoY\nYEb/jwf4tx6oGY/uRYPoWXMkrbFuHXuelja5Y5qhp6TMbtI6XXQqJWKcqansvCkpbAFx5Uq2TZKm\ncuUbN7LHv/5ix6alsZSHVguUlbGZtsHArhEOM/PTatnMuriY5anT05n5rlnD+rUQMaMXRXaOhgZm\n4D09zLDHx1mm6sQJ1nL3xQt2nspK9unQYJDN/PV6Ns5Nm1gd+rt3LA2Tnc1mnS4XM+jjx4HmZmDf\nPqbTamWLo+np7MftZikXgJ2nqYmdc+dOlnOvqGC9271eduO5dGlqwbS0FDhzhhl8JJ0U6QxZWvqz\nOR4+zP52ujm+ecNaHNTWspvB7dvse1tPnWL7l5KZ8g9dcZRG3Bdom5ub0draijt37gAA7t+/j5cv\nX8Ltdk9ddLbkNYfD4XB+y59adtz72RsMBvRHyjMA9Pf3w2g0Rh2ThAIgDofDWdbEPY1TVFSE3t5e\n9PX1QZIkNDU1weFwxPsyHA6Hw5kHcZ/Zr1q1Crdu3cLevXsRDofhdDqRm5sb78twOBwOZx4sSp39\n/v370dPTg/fv3+PixYvy9rnU3ysNk8mEwsJCiKKIbdu2AQA+f/6MkpISWCwW7NmzB/8tlRKSOVBd\nXQ29Xg+rdWqR/Fd6rly5gpycHGzduhVtbW3JGPK8mE2fy+WC0WiEKIoQRREej0fepyR9/f392LVr\nF/Lz81FQUICbN28CUE/8YulTS/y+ffsGu90Om82GvLw82TvjFj9KEN+/fyez2UyBQIAkSSJBEKi7\nuztRl180TCYTDQ8PR207f/48Xbt2jYiIrl69ShcuXEjG0P4Ir9dLr1+/poKCAnlbLD1v374lQRBI\nkiQKBAJkNpspHA4nZdxzZTZ9LpeLbty48dOxStMXCoXI7/cTEdHIyAhZLBbq7u5WTfxi6VNL/IiI\nxsbGiIhoYmKC7HY7+Xy+uMUvYY3Q5lV/rzBoxoLz06dPUVVVBQCoqqrC48ePkzGsP2LHjh3IiHQs\nmySWnidPnuDo0aPQarUwmUzYvHkzOjo6Ej7m+TCbPmD2ogGl6duwYQNsNhsAQKfTITc3F8FgUDXx\ni6UPUEf8ACA1lX04VJIkhMNhZGRkxC1+CTP7udbfKw2NRoPi4mIUFRXJ5aZDQ0PQT/Yh0Ov1GBoa\nSuYQF0wsPR8/foyqtFJyTN1uNwRBgNPplN8mK1lfX18f/H4/7Ha7KuMX0bd9+3YA6onfjx8/YLPZ\noNfr5ZRVvOKXMLNXa219e3s7/H4/PB4P6uvr4fP5ovZrNBpVaf+dHiVqPXnyJAKBADo7O5GVlYVz\n587FPFYJ+kZHR1FeXo66ujqsXr06ap8a4jc6OopDhw6hrq4OOp1OVfFbsWIFOjs7MTAwAK/Xi+fP\nn0ftX0j8Emb2c6m/VyJZk01q1q9fj4MHD6KjowN6vR6Dg4MAgFAohMzMzGQOccHE0jMzpgMDAzAY\nDEkZ40LIzMyUX0THjh2T3worUd/ExATKy8tRWVmJsrIyAOqKX0RfRUWFrE9N8Yuwdu1alJaW4tWr\nV3GLX8LMXo319+Pj4xgZGQEAjI2Noa2tDVarFQ6HA42NjQCAxsZG+Z9SqcTS43A48ODBA0iShEAg\ngN7eXrkiSUmEQiH590ePHsmVOkrTR0RwOp3Iy8vD2bNn5e1qiV8sfWqJ36dPn+QU1NevX/Hs2TOI\nohi/+C3q0vIMWlpayGKxkNlsppqamkReelH48OEDCYJAgiBQfn6+rGl4eJh2795NOTk5VFJSQl++\nfEnySOfOkSNHKCsri7RaLRmNRrp3794v9Vy+fJnMZjNt2bKFWltbkzjyuTFT3927d6myspKsVisV\nFhbSgQMHaHBwUD5eSfp8Ph9pNBoSBIFsNhvZbDbyeDyqid9s+lpaWlQTv66uLhJFkQRBIKvVSrW1\ntUT0az+Zj76kfHkJh8PhcBJL0r6DlsPhcDiJg5s9h8PhLAO42XM4HM4ygJs9h8PhLAO42XM4HM4y\ngJs9h8PhLAP+BzfKErnPSHSwAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# However we will take a slightly more disciplined approach here\n",
"# Note that Matplotlib wants colors expressed as 0-1 values instead of 0-255\n",
"\n",
"# Create a square figure\n",
"plt.figure(figsize=(5,5))\n",
"\n",
"# Plot both scatter clouds\n",
"# marker: self-explanatory\n",
"# linestyle: 'None' because we want no line\n",
"# color: RGB triplet with values 0-1\n",
"plt.plot(R, B, marker='x', linestyle='None', color=(0,0,0.6))\n",
"plt.plot(R, G, marker='.', linestyle='None', color=(0,0.35,0))\n",
"\n",
"# Make the axis scales equal, and name them\n",
"plt.axis([0,255,0,255])\n",
"plt.xlabel('Red value')\n",
"plt.ylabel('Green/Blue value')\n",
"\n",
"# Show the result\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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xcfTs2ZMrV67U6Qnfeecd/vnPfzJ48GDWrVuHVqvl1q1bDBs2zHpOYGAgiYmJ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\nEUkwJSQkJK6R/w9gYA9SfeAYdAAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# QUICKPLOT 2: Histogram of 'red' values in the image\n",
"plt.hist(R)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 66,
"text": [
"(array([ 3510, 5402, 7050, 11508, 15188, 16246, 15521, 28818, 8853, 7904]),\n",
" array([ 1. , 26.4, 51.8, 77.2, 102.6, 128. , 153.4, 178.8,\n",
" 204.2, 229.6, 255. ]),\n",
" <a list of 10 Patch objects>)"
]
},
{
"output_type": "display_data",
"png": 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}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# ...and now a nicer version\n",
"\n",
"# Make a non-square figure\n",
"plt.figure(figsize=(7,5))\n",
"\n",
"# Make a histogram with 25 red bins\n",
"# Here we simply use the abbreviation 'r' for red\n",
"plt.hist(R, bins=25, color='r')\n",
"\n",
"# Set the X axis limits and label\n",
"plt.xlim([0,255])\n",
"plt.xlabel('Red value', size=16)\n",
"\n",
"# Remove the Y ticks and labels by setting them to an empty list\n",
"plt.yticks([])\n",
"\n",
"# Remove the top ticks by specifying the 'top' argument\n",
"plt.tick_params(top=False)\n",
"\n",
"# Add two vertical lines for the mean and the median\n",
"plt.axvline(np.mean(R), color='g', linewidth=3, label='mean')\n",
"plt.axvline(np.median(R), color='b', linewidth=1, linestyle=':', label='median')\n",
"\n",
"# Generate a legend based on the label= arguments\n",
"plt.legend(loc=2)\n",
"\n",
"# Show the plot\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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}
],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# QUICKPLOT 3: Bar chart of mean+std of RGB values\n",
"plt.bar([0,1,2],[np.mean(R), np.mean(G), np.mean(B)], yerr=[np.std(R), np.std(G), np.std(B)])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 68,
"text": [
"<Container object of 3 artists>"
]
},
{
"output_type": "display_data",
"png": 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}
],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# ...and now a nicer version\n",
"\n",
"# Make a non-square-figure\n",
"plt.figure(figsize=(7,5))\n",
"\n",
"# Plot the bars with various options\n",
"# x location where bars start, y height of bars\n",
"# yerr: data for error bars\n",
"# width: width of the bars\n",
"# color: surface color of bars\n",
"# ecolor: color of error bars ('k' means black)\n",
"plt.bar([0,1,2], [np.mean(R), np.mean(G), np.mean(B)], \n",
" yerr=[np.std(R), np.std(G), np.std(B)],\n",
" width=0.75,\n",
" color=['r','g','b'], \n",
" ecolor='k')\n",
"\n",
"# Set the X-axis limits and tick labels\n",
"plt.xlim((-0.25,3.))\n",
"plt.xticks(np.array([0,1,2])+0.75/2, ['Red','Green','Blue'], size=16)\n",
"\n",
"# Remove all X-axis ticks by setting their length to 0\n",
"plt.tick_params(length=0)\n",
"\n",
"# Set a figure title\n",
"plt.title('RGB Color Channels', size=16)\n",
"\n",
"# Show the figure\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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nFi1aVG9BxUCVhvHpp5/qySef1NSpU/WrX/3Kb9mwYcP0zDPP6P333691/YqK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\n"
}
],
"prompt_number": 69
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A full documentation of all these pyplot commands and options can be found [here](http://matplotlib.org/api/pyplot_summary.html). If you use Matplotlib, you will be consulting this page a lot!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Saving to a file\n",
"\n",
"Saving to a file is easy enough, using the **savefig()** function. However, there are some caveats, depending on the exact environment you are using. You have to use it BEFORE calling `plt.show()` and, in case of this notebook, within the same codebox. The reason for this is that Matplotlib is automatically deciding for you which plot commands belong to the same figure based on these criteria."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# So, copy-paste this line into the box above, before the plt.show() command\n",
"plt.savefig('bar.png') \n",
"\n",
"# There are some further formatting options possible, e.g.\n",
"plt.savefig('bar.svg', dpi=300, bbox_inches=('tight'), pad_inches=(1,1), facecolor=(0.8,0.8,0.8))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 70
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizing arrays\n",
"\n",
"Like PIL, Matplotlib is capable of displaying the contents of 2D Numpy arrays. The primary method is **imshow()**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# A simple grayscale luminance map\n",
"# cmap: colormap used to display the values\n",
"plt.figure(figsize=(5,5))\n",
"plt.imshow(np.mean(arr,2), cmap='gray')\n",
"plt.show()\n",
"\n",
"# Importantly and contrary to PIL, imshow luminances are by default relative\n",
"# That is, the values are always rescaled to 0-255 first (maximum contrast)\n",
"# Moreover, colormaps other than grayscale can be used\n",
"plt.figure(figsize=(5,5))\n",
"plt.imshow(np.mean(arr,2)+100, cmap='jet') # or hot, hsv, cool,...\n",
"plt.show() # as you can see, adding 100 didn't make a difference here"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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N3/iN35AMEQY5m/RGOjCZkKYVjSI17WmaJO1pBsu2lw1K056mY/7N5zB+UDPO\nUYhLMwnSiDbw8x8FxqjsCvOZ5hhNIMDvNILUtknz2WZYi95r2ibHd+lcYA14TEZozqdGysBHe2Mf\nGLK7ffu22L2i0ejPlZRaHQCGmZpmlHqxRxEc/89F0gs5GAzw5JNPYnFxEaurq9ja2kKv15PAYGB3\nEaemppBIJHDkyJH77ES0Y5HQdGmkYDAoRv9wOIxbt24hm82iVCrB6/UKAqIDArhXQJOEoNW2hx56\nCFtbW7h+/TpisdgQ02NoC4t9Mh6OYSE8BYye1WKxiOPHj8umYXZFsVjE9PS0eEUBoFgsIh6PY35+\nHrFYDDs7O8jn84hEIrhz5w6OHz+O7e1tnDlzBhsbGzh9+jQsFgsKhYI4YQqFgsT5dTodCYimrZKV\nVIBdZxGzTnQLBALodDp46aWX4PV68dxzz0m5ctKFSTea6QHDeaLa1jQq/1rTHp+rv9NMVT+b340y\nq+j7tbNrlIA2r+lxkq5NLcmMANBNo1S9J3S/TLVS/073l9c0ctV2Rt5j7ju+S2tupilLFxUwEZ5m\nmD8Ptz0wZsda+Cw4GQ6Hh06dGsW59eSZdhXeR8luEoiGziQK07YTiURQr9cRj8cRi8VgtVpx8+ZN\n3Lx5E+Pj43j++edlIzKlymq1SkUQqmG6fJHNZpPAYxIAQ1sOHDiAt99+G91uFx6PB5FIRBir3W4X\nryWdG16vVwKY2+02fD4fDh8+jJdffhmtVguVSgVTU1NSOYQ5t+12WwKA7Xa7oFWWeff7/VLmiR5S\nMsxMJoNjx45J39LpNCYnJ7GysoKZmRmJ1Xvvvffw6KOPYmxsTFLQFhYWYLPZsLS0NHQsX71eF6br\ncDikGMHk5KSotQ6HA4VCQbJTaA+jU0jn5bpcLrz22muYn5/H+Pg4/uzP/gw+n+8+u5mmK665uUm1\nY8C0t2m0MSoxXtOiplfTVqWfqZ+tnQlWq1WCefX9muGNus5moiLNtDUzNMegnzdKdRzFTEehTfOd\n/Fvvac30zMwofb95ne/h3tBA5KPaA00Xy2azWF5eFlUmGo0KIZlVWUmoeqI1/NYZCvwkAZk6vUaA\nWoWIx+OSjcDE/vPnz+P8+fO4dOkScrkcgsGgoCaTSKly8f1M2icTZjxds9lEv79bPunkyZNYWloS\nmx9DP5gT6/V65Zl0OugjDrvdLj7xiU/g8uXL4ugYGxsTG6Pf75fYv3K5jNu3bwsyY/jKzZs3MTU1\nhXa7LeWBcsYIAAAgAElEQVSiqIZbLBbJxKD9bXV1FYlEQiqlFAoFzM3NYWtrC5FIBKVSSVLJfD4f\nQqEQfD7fUCWXsbExNBoNKTzKrAnaOhnqwqrLWnWkQ4jrwDE3m01sbW3hm9/8Jp5//nmcOXNGmI6Z\nScE2yg6mGd0oBmeqmOYzTSZibmrNsPSzTFONRk7aBj1KezELJ5jOD70PTKasr2ngoMfAZ+6Fpkap\nmPz9KOFi3qvHbjJaM8Vzr7k3mbbZHmg9u36/j83NTUFILpcLgUBAvjellakyaCamF9T8/6jvtMes\nUCjgxo0bsNvt4jAgEmSg8NmzZ3Ht2rUhaWIyNwCSIsPaW0QtLGkUiUTQbrcFcZ09e1bKPZEZDgYD\n8f5qDyufw1AM9qVSqeDJJ5+E2+3GjRs3kE6nEQ6HcfDgQUQiEQwGA0nVOnv2LJrNJtbW1lCpVNDv\n9zE9PY1EIoFer4eFhQURRM1mE6lUCsFgEPV6HeVyGVarVRwr5XIZ4+PjeP/993HkyBFBsbFYDNvb\n23jiiSdw8+ZNRKNRtNttuN1uyZ3N5/PweDxwu90S08cipn6/H4PBQOZOM0AGOHMeyPwPHDiAXC6H\ndruNW7duYXV1FWfPnsVnPvMZxGKxofNfybh01swoREI6G+XdHUVLbFooa+apr5t5pKZ9y6R9bcsz\nmYR2DGj6NPeKRriasfPaKMY/aq+Nmh99jzl+k7mafMBiuf8MFz23msGbDE3Pk2mrNdsDY3bsZKPR\nwPLyMgDI5vZ4PPdxdU2so6QVJ0ovsFZNgGHJ3evtlq1ZXV3F3bt30Ww2hclYLBZBDVarVRBJo9FA\nsVhEOByWRdRng7Kck3Z6NJtNiSOkujoYDAR93b59G9PT0wiHwyiXy6hUKigWi6hWq2L4HwwGcppY\no9EQVZRZG4VCAbOzswgEAvjkJz+Ja9euodPpSDweVdNWqyVMo9ls4tSpUwCA06dPY3FxUfJoV1dX\n4fV6YbfbUSwWUS6XpfwTALz33nvI5/NwOp1YW1vDgQMH4PF4cOLECWxsbMjBLisrKwgGgygWi+j3\n+3IoEJl1s9nE+Pg4bDabFCHgMYz6XAuGr4TDYVHtWEqK60YUy/uLxSLefPNNXL58GU8++SR+8zd/\nU5AscI+hmUVgNdrRzMV0WpiIhr/XSEj/f5R2Ytq0TGO7qbLqaIRRXlX2n4190+/mdQprvSf0WPQz\nRvVZj3mUF9QsK6/nRM+dBh3meEfFR/J3JlLW+3yv9kCZHaVStVrFxsaGnK0wOTkp9irT06MZmCkx\ntFQYJSE1MVksFly7dg137twRhNbr7VYWJprQXq5EIoHFxUUcOnQI8Xh8KMUGgNSeazabEgxst9sR\nCASGnBblclkyC27fvi0Bx71eD9FoFOFwGLVaDYVCQcJTUqmU2BInJiZk/I1GA6VSCYFAYKhM++zs\nLF5++WUkk0lJz2HAMftRrVbRaDQQCoXE+7ywsCBhN8xo8Pv9uHbtGp566il0u13EYjGcOHEC/X4f\nV65cQTweh8vlkhp7Xq8XW1tbki6WSCSwsbEBq9UqzG9tbU1QHQsNhMNhmU+GtrjdbkxMTAw5fPQB\nz/3+bhxgsVgUR0u320UgEBAzxGAwEBX/93//9+9DH6OcD2ZGxSi1y6Qpfd1kOCZy00jP/K2J1kxm\nxnfqfunPUcxXM0/tQeUziXI1ehqFRvdSiU0BAdxDoqbHWr9DzyO/IxAxnYd6fFql1lWkf16z/cVf\n/MVf/Kfv/pjaX/7lX+L06dNDtpRut4tqtSqpTsFg8L5F0YNk09+bi06GakrhXq+HW7duIZ1OY2tr\nCxaLRWK5qPJls1kUCgVsb29jYWEBrVZLClTy2EBmMdC+ReTAPjE2jUGy/X4fTqcTpVIJ7733Hra3\nt4VJcRGZlsYwi83NTZTLZbRaLRQKBTm2sN1uI5fL4dKlSwiFQpidnZU5I+JrNpuIxWISu+dyuWRO\nMpkMksnkUJ6tw+HAO++8g0KhIOMBgFwuh1KpBKfTiVgsBr/fL8iYISAsQECvMhnt5uamzEMkEhHU\nBgDJZFIQGqsi22w2ieVjmArpgeXjS6WSZKFsb29LsLcWQCxUAOyW0drZ2cGlS5fwxBNPDNEPN7qm\nD01LbCY60qqs/kea5G/MayYK0mhvFAMgIyH9aOebfr7JRNhHfmeiR/39XntHM2nN0PSe49xps4Ce\nT5Mhcyzm2PT9+rrZDz0GHXLG69/+9rexF0t7oMU7uTG5kIVCATabDaFQCB6PB2NjY7JB+RstqfRC\nAMPndmoUpwlhZ2cHzWYTd+/ehcvlQiqVEiM5AFFDp6enBWHcuXMH1WoVyWRSnAt37tzBxMQEPB4P\nJicnJW+23W6j3++LjYtxcyy5lM/nUavV4HA4MD4+LvF77DPVZ138koyHObmFQgFPPvkkut0uDh06\nhEajgVarJWiY6W1ra2sIBAJSQp3NYrHg5MmT2NraksBpZomcPHlSGCxtqIyJe+ihh6TEFQ/+yefz\naLVaUs5qcnISALC4uCgIiyiPcXl0XFSrVXi9XpTLZSkUwL53u100m01xCPE8XaJYrsP4+LhkYDBo\nud1uo1AooNlswmq1olQqiXf2pZdewkMPPYSzZ8/el3UA3I9e+GkG7pqITtOlmbtK2hyFCvX7dayZ\nFuAayZlFAkY589hP7fgYZcPjfZoutOpusViG9qdWp805IhPW2VCmo8REvcD92VN6HvRc66bXZJQG\nt1f7hZgd0QTVwIsXLyKfz+OLX/wiVlZWMDs7i3/6p38SFUU3M1WLE10qlbCxsYFoNAqHw4FIJDLS\nJqAnwVRF9PeUNJ1OBwsLC1hcXJR7SqWSIBTa1nq9Htxu95DhOp1O48SJE7BY7h2G4nA4sLa2homJ\nCfEiDwb3PIYa1aXTaVGPfT6fhEuQmayvr4vqa7FYJKyFh/L0+314PB6cOXNGgnUZ13bmzBl873vf\nQ7VaRTAYFCKLRCL44IMP8P7778PpdOLo0aOiUnOTMwXN7/fD5/Mhm81KgPT29rYE+DabTSn7NDU1\nJYzu4MGDsFgsQ5kRq6urSKVSIszIqKl+ApBkf8YwxuNxVCoVsUOyssvm5qYEdGvve7lcFlsm4/8S\niQQ2NzeFmRKd8r3Mz2Xq3erqKj73uc8NMS3gflSnkTpwvx2MITT6fr3xNCrStKo3Jr/THmM+y3yv\nZoJkLvq5GkHpZgaYA/dnMmjEpE0efC9/q8OAyHw1ojOfx2YyPB0qoseh14R7kPuRdKDHo9Xbj2q/\nUAbFwYMH8e6770q5bwD42te+hlgshq997Wv4xje+gUKhgK9//evDL7VY8IUvfEEGRunBcAmPx4O5\nuTkcPnwYU1NTson1RHJRRuUEAvekQ7VaRbVaxfXr14XhMKGfdj/tlWPqFycvn8/DYrEgFAohHA5j\nfX1dYtKYL8rg12QyiXA4LKEWVqsV8/PzaLfbCAQCCIVCUs1D2yJtNpuoyV6vV8bldDpRLBYlVGRm\nZga9Xg9ra2tot9t46qmn0O/vZlKMj4/j2LFj4lD54IMPMDMzg06ng+XlZUFziUQC/f5uKXm32y15\nrblcDsvLyzh9+jTa7Tay2Sw2Njbws5/9DP1+H8eOHcOJEyfwyU9+Uoiu0WhgaWkJtVoNvV4PqVQK\n5XIZp06dgtvtxtbWlpxl4XK5UKlUEI1GUa1W4fP5xN7ocrnEdklPLJ0xnCOXyyXqK7Abp5nL5SQ2\ncHNzU5wffr8fxWIRi4uLMtdEllarVYRTLBbDl7/85fvKC5HORiE806yi7zMRkGaiupmqqqZbzUBG\nqZcaHPC6qWKbWs0oBm7aDDWT1HYzPY6PQk8mc+d7RtkeNeo0n20iZRMhso96PHo+P6rqyS+sxpoP\n/sEPfoDXXnsNAPDlL38Zzz777H3Mjh3Wv2USOLDrxaRUp11M53mymRPGTwbtXrp0SZwGVAFZIBOA\noDqbzQaPxyOR+lSh3G434vG4pGp5vV5MTExIGXOmahHppNNprK+vY319HdPT0+h2u1hbW8MnPvEJ\nIQBmAXCTDgYDOXOCNjtdCoqhH3fv3oXH48H29jaCwSAmJycFmVJ1zmQy8Hq9qNfr8Hg8mJiYkMDj\nTCaDra0tFItFcQQxlYing3m9XlSrVamEXK1WcebMGUGspVIJlUpFYuIqlQpqtRrS6TSsVqsUAmX1\nEgCSwuV2uwVBhkIhKfNOL2w0GpXKLuVyWZhRt9vFnTt3EA6H0e/vhsSwD16vF7VaDf1+X+L4KpUK\nstnskN2n2+2iVCoJPaXTaXGcfPe738Xv/u7v3scQNG1rpmUWGzBTtbQWotOgSPMmg9EoTjvdRiFJ\n0oR+v0Yz/Ns06JtIkX9rxwS/Z1953QQUZt9MEKK/19qXZtR6vKZpSo9ZP1f3xUR1Jl/Yq/1CzM5i\nseBXfuVXYLPZ8NWvfhVf+cpXhJAAIJFIIJ1Oj/wt7Sl6kBqaU53l0XyxWExUQy15TFsI7UuXL1/G\nYDBAOp2WjACGidBGxcmmasOJ5xkLJGKqVQwtGRsbw5tvvomjR49K2tVgMJDQiHq9jnw+j5/85CeY\nnJzE5uamZAGwAkij0RDbFLAbn8dzJ/Th1NwwyWQSly9fxpe+9CXp4507d5BKpfDss8/K+FZXV7G9\nvY3Z/whFocczlUrh4MGD+MlPfoJjx44hGo1KCEooFBL0ZLFYhFH2ej1cuHABhw4dklPPbt++Lfav\nSqWCRCIhAc6bm5uYmJhAJpNBp9NBqVQST/DY2BgGg91QI2Y+RKNR+Hw+OWioVqvB5XIhEomgVquh\nWq3Car1XPZnOC4/Hg2q1Cr/fj2QyKSl3zCqxWq1yPgcPEiJz5obudDrw+XxYXl7G3/zN3+BLX/qS\nnMNLJqnVJa1CacSyl6oGYEj10rY4rdpqxkdUp7/X8XW8j8BAMxYzpWoUU2U/zBg/zVD4PH7HSAJ+\nr/tg2hdNxGkiWP08/UzzHu1M0cxxVNC1DnH5eWrsL8Ts3nrrLSG2T33qUzh+/PjQ96OkJdv8/LwM\nKBaLSd0zLV2q1Sqy2SxyuZyklZn5jnqSm80mfvSjH8mGHQx2Q0iWl5cxMTExlFDOiSRjIRMyiYat\n2+1KYG673UYsFpNSShaLRdCi3++Hx+MRJvDuu+8OlYyyWq3CSFnok55IXQqJUnB9fR12ux1TU1Oo\n1WpwOp2SVZFKpVCtVkV9dblcSCaTUvKeebY6BW1mZkbKJHHT5/N5KTtFYmIA9GOPPYZbt24Jo9/c\n3MRTTz0lsXJk/ozHI8piZWen0ymxcxrt0eHC2Do9bnqoudmz2aycg1uv1xEKheD1erG9vS1e81Kp\nJPGQtNsxvpH90Gf4soQVnTv/5//8H5w4cQKPPvrokDrKdWUzM3RMRmHa/vTm1XaoUerlKBWYa8iM\nEo3udH/0c2hzM0EAbW0m82IbhRQ1SuNvgHv2Rd7HvaudhyZ6JePXDg0KDY3WtLDRe5xNm6quXr2K\n9957b+j6Xu0XYnbJZBLAbiXZz3/+87h48SISiQS2t7cxMTGBra0txOPxkb89ceLEUOf1ADXzYogE\nPbRcaE1c/f5ucPCPfvQjOJ1OBINBKRc+MzODn/70p1hbW0M8Hh+qnMEyTtpeQecCwyB0Ghil0jvv\nvINDhw6JJ5V2PoZD0IP69NNP48KFCxJnduDAAQmcDoVCsFgsgiY8Hg9CoRBarZYUSHj77beRSqUQ\nj8fR6/UwNja2u2j/0ad4PI5QKCTVUHg+xfnz5/H+++/L/NBWxSR8HQbD7JVWq4VkMolOp4MbN27A\n7/fDbrfjueeeQ6PRwOrqqnhPqT6GQiFks1k5YSwajSIQCMjBQNykc3NzaLfbiEQiErZDYicTzWaz\nUsTTbrfLYd5utxvJZFIQD4sG8OQzCsv19XWZP553wX/0RNNUQLsp583pdGJhYUFCeljAVFe6MT38\nmlbNkAvSkKm2AqPVMo1U6HWmTbRQKGBzcxOZTEZo1CwSS+E6OTkpGgRRKd/JOafpZFQcnKkljVKD\n2U+dOqe/43401e+95muvqAmNok3kx/dYrVacO3cODz/8sHz393//93vyq/+2g6Jer6PX6wlxv/DC\nC/jzP/9zvPrqqxgbG8Of/umf4utf/zqKxeJIB8UXv/hFsaHpSev3+3KyPGuyTU5OYm5uDrOzswiH\nw0OH0PR6uwdTv/XWWwiHwxLmwMR6xmxdvnwZ6+vrSCaTcoJWLBaT3EsSAnAv7YQEQsJi0vqtW7dw\n+PBhQTb0OpKxkJi2t7dx+fJlrK2tidrKMkcnT57E7OysnNnA1KhwOAybzSbpWKFQSBg0j1mMRqND\nktpi2a1PR2nqcrlw4cIFibHjmIrFIur1OgAgEolIiXRKXFZD6fV2qxtz0+bzefzwhz/Ezs4OotGo\nVDEhIuY7y+UynE6nBPqm0+mh0Jx2uy2lsMhkqFr2ej2EQiE5CY1zyzg85s3W63VxXDQaDfT7fbRa\nLVy/fh3FYhGxWAwrKyuw2+3Y2NiQTUVbJtVg9oFMn2jP7/fj0KFD+MQnPoFTp07dp55pGh7VtD1N\nq6ym3WyUmpfL5bCwsIBsNivj5Dx3Oh1JMdRrRualmQrL5h89ehSHDx8eGc5iMjHTvsb79HPJ4H6e\ns0A3zehNRMm5obOQhXBpG6Znnnnl7LPf7xctRJdgc7lceO655/ZEeP9tZJdOp/H5z38ewC6c/J3f\n+R288MILeOyxx/Bbv/Vb+Na3voXZ/wg9GdV02SSmAFFyE3kwIb9QKCCXy4kNiuiu1+vh2rVruHjx\notRXox3MYrEM5VbOzMygUCjg2rVrACAnV5FZcVL53FAoJHFhZCoul0uKjrL2HGF5v9+XMBGOr9vt\n4td+7dfQ6+2WQy8UCrhw4QKWlpaQTqfx0EMPwWKx4Pz588jlchLrtra2hnK5jE9/+tNi99zc3MTB\ngwcxMTGBUqmEubk5dDodqchCtZ0oFNg9KyOXy2F2dhZ2ux1jY2MoFAqiVmrUx6MSde4ppT7Twtxu\ntxzA7fV68fzzz6NYLAIAVlZWBKESoYRCIdjtdiSTSSnfxLAWq9UqRRe4AQDIKW2FQkHstaySEo1G\nEYvFUK1WkU6nYbPZcPv2bUGDdrtd0t54IFCpVMLU1BSy2awwdKaY+f1+SdGjYKzX6/jggw/EZnjm\nzBnJQ+aGNe1odCzpsZiOBl7XzyFj5Ilyt27dEnpnIHW73UapVAIAoUki+1KpJKX8SXfM/Ll79y6W\nl5dx4cIFPPXUU5idnRXNhKYajfKJsmiL05kWptNDp2hppsgxEnVp1Eihw/cylOmDDz7AwsKCHHVQ\nr9dlPckAKXRZur/ZbIptNhKJiF16fHz8I3nWf5vZHTx4EO+///5916PRKF599dX/1DM4IG2UJTLi\ndcL5TCYjZ45yM/b7fVy/fl2YIO0UJHyqk7VaDcViEePj45iensbS0pKEW/h8PjGU6wwD4F7hSHot\nHQ4Hbt++LZV8uZhaHWIye6PRwNNPPy3eXapIMzMzePPNN3H37l0cOnQIJ06cwM2bN7G1tYXTp0+L\nOnns2LGhEBeexKXDZvx+P5555hmsrq4KCqV38IknnpCKKxwLP3lATiQSgcVikdCWUqkk6JaIh9Lz\nmWeewZUrV0TlnZ2dHZLQwWAQyWRSDt9m7isZDyspFwoFjI2NoVqtIp/PA4DE4vV6u2ffAhC7HB0J\nTJ+zWq0S1FwqlWCz2eTwcNJBPp8XVZDZHh6PRwSs1+tFo9EQdVA/E9hFLrlcDjdu3EC328WxY8cw\nPj4+5DjQ9jYyCX5v2t30/zXzY9Xpra0tbG5uAoCUuGLYVKFQgMViQTKZlOIQLCRBc4xmWpwzi8Ui\nGUk//elPcfjwYczNzSGVSglTJtMm0DDDRLRnWfdbq96kFXO82obX7+8G2S8uLmJpaUmiArLZLPL5\nvAAOFnygA4RRBnwH+0eBRZME9yCP5dyT33zkt/+DjZ4Z7SkieiJHp4oCAIVCAVtbW0OH1PzsZz9D\nrVYT9ZCE5vV6hSlmMhlUq1W4XC5MTExIuaNcLofFxUXcunULMzMzYk/TkJiLxVix69evDxmcqS4D\nEFXIbt89IzUcDiOVSqHVaolXkepoPB7Hs88+K6YAp9OJY8eOYWpqSu4plUpSwv3pp58WIuaBP3S0\nAJCinFQnaaxnwDc9VvQK0764ubkp6jyDqhuNBrxerwRz1+t1KTAwGAyQz+dRLpdRKBQkbITpaPpE\nsdXVVTz++OPioKBtk55rnaK2tbWF2dlZALvmEabg6VAMt9uNbDYrZ9ZarVasr6+j0+mIfZEOpEaj\nIQd+0/kzMTGBzc1NQRVca8ZKjo+PY2dnB9lsFhaLBfPz84IqVldX8eSTT2Jubg7APeambWJkOPxe\noxqN9IBdRrCxsYE7d+4gl8sJTbHUP5k2APFkB4NBSRmktsE14vMbjQbS6bQABIvFIrnPi4uLWFlZ\nQSKRwDPPPHNfyX4dIsPrfK7ZNLMn09MqMcfO40Dv3r2L9957Typda4ZmIkfa0imoyUAJcBg6xrhJ\nCmQ6oj6qPTBm12w2h6p3MB6O9i+iPQ7EYtmtdLuzsyOnUi0tLQ15xUxYzdxWOheIsCyW3RiuUCiE\ngwcPotvtYmFhQZAIU9UCgYBMfL/fx/nz5/H444/j8uXLKBaLOHTokKQ3ceHz+Ty63S4OHDgwFMfH\njIjV1VVMTU0hFovh/fffRzwex9mzZ2GxWDA2NibpZIwly+VyQ5U9tre3xVtMYUEmz09KeRroWbjz\n1VdfxalTp8TOce3aNUm/ooc3l8sBuGdmoCcTgGRI5HI5HDp0COFwWGxunLtGo4Ht7W34/X44nU6p\nQs3YO86R2+2WeLp4PC4HbetCnaQL2gLHx8fFywpAwla4YViEgQHqVD8tFguy2SxqtRqCweBQ1ozX\n60Wr1cKNGzekDH+xWITT6cTq6qqcnXvx4kW4XC6Mj49LrrPe6JpR6FAVMyavVqthfX0di4uLKJfL\nwlB4YBGD1ykE+DvmRDebTYyNjYnZhv+oBtK0QtRGhw6FeKVSQavVkrONTdox7Y3aGWOGqmgnAumf\ne2xpaQlXr17FxYsX5TAoFo/lmLjOtJ8ybZPfEb2RbnQdyMFgIPuEbVSmlm4PjNkxDooIhbXc6JWh\nzUhPaKPRQD6fh8/nw/b2thRsJKHx2L3NzU0heG5ibdDl+QZcwEajgampKTlcplariZF0Z2cHJ0+e\nxLFjx8SRcObMGbz22mvweDzCLK3W3ZPuiQLNOKRGo4FsNivZFFtbWxI7mE6n8cILL+D1118XNdhq\ntWJzcxPtdhvz8/PCBKampsTreOvWLdhsNsRiMXGyALsHVb/zzjtYXl4WG0+z2cR7770nqCYQCODf\n/u3fhjy9Oi6PYTkAJEbt+vXrcj7s5OSkEJxmMOzHyZMnhVEw/IXeVXrViS50dgXfR1uiDmFwu93I\nZDISNsLsD2D3ACc6V8i82+02arWabCLaYOkFpsrodrsxNTWFQqEwFF9GpJVMJlGpVPDaa6/h1KlT\nOH36tDCTj/JeakN+p9PBysoKlpaWBHkT0VSrVXFcsVAEadPr9SIQCMDpdIodkwcWabMPU/+43nQc\nEYXpMzvK5TK2t7fxyCOP4OGHHxZbnh6PRqujxmaGyfR6Pezs7GBtbQ1XrlzBjRs3JLjdTL8k+qPq\nqh1QfDYdEtyfFIQUwpVKRcKW+Dzaj/dqD4zZ0Ripjw0kc6MKS9RHfZ05mrlcDp1OB4cPH8b169dl\nMQlt4/E43njjDZw4cUKKZupDhLmZKFEef/zxoTMbuDF1nT1KRIaNPPbYY/jwww+RTqcRjUYxPT2N\nQ4cOIZvNYn5+Hrdv38bRo0fR7/flfIVcLodWq4WxsTEphUTj85/8yZ/gS1/6EorFongSw+EwLBYL\nVlZWhs6B8Hg8aLVaiEQi8Pv9Yq9YXV2F1WrFm2++KZ7IYDCISCQitrC3335bvJ4zMzNwOp24du0a\n6vU6PvvZzyIWi4ktk4n8q6uriEajYn/7zGc+I/Ymho7wZLVarSZHPdJZpCsrU91nUDBDfCKRiDBN\nIk9dZotokILLat09LW1ubg65XA7ZbFYcDrRVccMwfo/vGgwGyOVycvBQNpsVe6c+cc1iseDu3bvo\ndDo4cuQI6vU6rly5ApfLhSNHjgwhHTNGTduds9ks7t69i52dHbG3eTweBAIBcZxoVZzqOHOIiT4B\nSGUb9jGdTgtqJW07HA4xBQD3mAdDerrd3WMzf/KTn2BpaQmf/exnZV00M9M2SgIK0+5oseyW5Lp5\n8yb+9V//VcwCACSuEthVx6maEpkzUFzbEB0OByqVitA+TTSxWEyiFPr9vphGuMb/mVJPD7SeHVOd\ndLYAkRwwXKyQ9rBGoyGGyKWlJWQyGUnrCgaDOHjwILa2tpBIJMQoTWZK+xHtV5lMBsFgEOl0Gj6f\nD5FIBMePHxdi8fv9GB8fx/Hjx8X7w35MT0+LdHv00UfFGxoKhZBIJPD666+L+kmbFuE3PZck5rW1\nNWxvbyMQCGBtbQ31el0yDoLBIBKJBKampqQM1ocffig2LfarUCjge9/7HtbW1nDw4EE4HA6JTczl\ncpI7euTIEUHQm5ubkvXhcrnw/vvvC3JLpVKiBrPU0uTkJDY2NlCpVOD3+5FIJLCzsyPl2UulEqrV\nKk6fPi1ZGQwvYXqcrupCBM9PhhpQKNEDx4IQ9MLxbI16vY5MJiMhOv1+H8lkEpubm3I2LVU/qqyV\nSgVut1uClbkOrHJstVrFzsm1Y1oea+pdvXoVVqsVR48eHVJlNaKjrXZxcVHQKIunhkIh2Gy7FaZJ\nMwylKBQKUs6KjIbOHDJXxgkSuVKt1qafarWKTCYz5PVlEQgirW5391jKf//3f8ezzz47VGFIh4to\nJ4ZGsa1WC2tra3jvvfdw+/Zt8XDTpEFhxbhVHatIpkeNjnuVBSHoKCNNUIXlXqbqr7Oqfl57YMyO\nHpJR69UAACAASURBVFMyPJ4qRXsT7XSDwWAo2prqItWmqakpCSngSVi63hzhPuP1BoOBJIhvb2/j\ngw8+wOTkJCKRiBiYOdlra2t48sknxWup4TcLc1JV0wz52LFjOHnyJNrtNr7//e/j937v92CxWFAu\nl7G4uIhWq4X5+XnZ2Iyf4ngZLU+70nPPPYd4PI5kMimMjTa+fr+Pr3/963A4HJiampISTXfu3MHs\n7Cxu3LghwmFqagrj4+OwWCzY3NwUW2MwGEStVsPFixcB7HpCGQhMgsrlcrh9+zbK5bJUamafyXi2\nt7dx/vx5xGIxkc5EdDr9il7zXC4ndjiGrfD/pAka5vmM+fl5CdfgJiQqBHZzj5l2p7395XJZDu8m\ng2i1WkM5s3a7XexLwWBQvMb9fh9ra2s4fvy4MKkLFy7A7XZjenoawPDxgN1uV+xyW1tbgmoYx8h1\nBXbtTFTrmVpJDzm1GaJdMmIKOtNJoePdWAuR80n7LpkObX6DwW5xU5/Ph/Pnz8vaAPcffKNDbQqF\ngjgeaBenB57Ck2haO9BoO6fqrOkcgNh4KaRIr1Rx2T/SnmbQRL978pxfhGH9Io1SkkRKTm+GSpgS\nRked01NptVqxsLCAmZkZzM/PS1gKN/L4+LioQA6HA5/73Odw8eJFkcAsDbSxsYFSqSTFM3UOIg2r\nlI6stLG+vi7eMiIhxum53W4888wzUr+u3+8jFovh7t27Un3YbrfD6/Xi3LlzuHTpklTtZRjI+fPn\nkUwmRW1pNBqo1+tD1+bm5gRtWSwWzMzMoFKp4OLFi5iensbhw4fRbDbleEgiqU6nIw4IZhowSf/i\nxYuYnJzEuXPnsLy8jOvXryOdTuPxxx+X31J9WFtbEyQ5OzsLm80maXU0R9Cep73DDHim/Usbws2c\nTBYwZRA4S0DRlkWESadKq9WS0AwyMKIrbbQnQmARBCJ0hsIAEMfRnTt3kEgk5PtXXnkFL774IiYm\nJgDsopB8Po+bN28il8uhUCiI1sK+cKM7HA6xtbL+nsVikVhRVrUm46UdjgiGJayIiDSDKpVKqNfr\nIqQHg4GsF7DLFDKZzJAAf/3111Gv13H+/Hnx8tNcwD3Q6XSwtbWFd999F6urq/JOn88n5/7WajWx\nvbKYBgBhsHQQcm1Z0IEInnPFuFAKVdKFDqTmGrFvXK89ec5/k1f9wo3MThsoGUdE5sJQC04CcE/9\nHR8fx0MPPYSdnR3s7OyIujcY7NaMI5GNj4+L+hKJRHD48GEMBgPJBZ2YmMAPf/hDLC8vS5+oKj/9\n9NNDREQkYbfbsbW1hXK5jEwmg0QigUgkIrm8rVYLn/rUp2QzcTw0zr/99tsiWRn3dvz4cWxtbYk0\na7fbuHv3LiYmJnD48GEpx05iYVWY8fFxfPGLX0S320W5XMbVq1dx5MgRXLhwAb/0S7+EWq0mp4qx\nWgqrtiwsLMiYKYUZrpFOpxEKhZDL5bCysoLbt29jfHxcApi5dsxKaDQaOHPmjEhep9MpRMuqMHRW\nlEolye5g8r4OPQJ2GQe9d3ROuFwu7OzsSI6sDn2hDY9IjEHBVP2Jluj8IYojI6TdjuE6dBhw7Ymo\nQ6GQxFHW63W88soreP7555FMJlEsFnHx4kU5XY1Mm5t+YmICgUAAuVxO3lEulwHcO40uGAxKBkq1\nWkUkEhHmSEaXy+UkU4XXHA4HyuWyHKbE4zu1BkVhTOagwz6azSYuXbqEZrOJX/3VXxXhTnWy2Wzi\n9u3buHr1Ku7cuSMxr61WS+zGtCnTRkuwQHshADEbUCCR8ZGJ6ThB3k/EpsOrOAbmaBPpfSTP+XhY\n13+90UbHcIFeryd2Gx2NzsnQernNZsPU1JTUevN4PIhGo0O2pU9+8pO4ffs2Njc34fV6MTs7i5mZ\nGfHO+Xw+ZDIZ+P1+vPDCC6jX6/jud7+LN954A8eOHRN1jMb8O3fuSEwfUckXvvAFDAYDSYuiSmOz\n2UTVZon3I0eOiAE9Ho9jbm4Oy8vLKBQKAHaLKrTbbbEFnjhxAk6nU4KPads7duwYDh48OJRXyM1O\nRFmpVPBHf/RHyGazQhDZbFbUU4aY6MNruLmZZdFsNsUbfe3aNVitu9Wb5+bmhkwEDKdgeh6LiDKs\ngsiKBEnbJYAhE4UWCMC9YxLJAOk8YN4r38Py8xsbGxKO1G63xUaXz+clgFgz10KhIB5aAMJkufGt\nVis2NjZk01PdymQySKVS4q3NZDJ4+eWXcebMGayurkofyehYNmz2P6rQ0POqT0ojmqODjZ5Fq9Uq\nYVYMH6LH32azSTC5roQNQOaadkZ6RLmXWq2WOAA4LgqvW7duwW6348UXXxSBs7CwgMuXL6NaraLV\nasHn84lDRe9R9p9aF+mfxSJYibzT6Qjt8XlkgFbrvWM2ddpos9mExWIRtZ3qOOfN7/eLZ36v9sCY\nHSE4iZgqGuGp3gjaLU5ozVLbXDhmShw4cECk+vnz57G2toZUKiUGbhpx6b5+5ZVXxO7z2c9+dijk\nhQU1nU4npqam8OMf/xgnT54UJg1AQlXo5dJZAA6HA6dPn0ahUMDa2houXryIzc1NHD16FKlUCrOz\ns6hWq3jrrbdQrVaRSCTw+c9/Hk6nE4cOHUKxWMStW7fw9ttv48qVK5ibm8OTTz4p9fWoGjJm0Waz\nYWJiArFYTEqlUwI+8sgjom4y7YoqyYULFxAIBCQvt1AoIBqN4qGHHkK/v1v6fGxsDJOTk8Lger0e\n8vm8FEednZ1FrVbD+Pi4nElBQzXVFyJ3ogx6ALm+ZDxEct1uV5wLwD21iGXatWeeNi+GH2gVXWsP\nOgmdqjNVINJXu91GIpEQmiK6ZMzf1NSUOFtok2LuM726TOFyOBxSV7BYLAr6CYVCiEQiUguQZhN6\n7Jk2yfezDBmdAHSiEbnNz8+Ls4qmA465XC5jZWUFgUBAUJ7Fcu/cFY6FppMPPvgAsVgMU1NTuHHj\nBlZXV5HNZu+rKak/iczIwFiGi2ch12o1sVsWCgWhUQo54P5CDbxOzYMaAIXXYDAQu7dW0/dqD5TZ\nkeApQTgZOryCqI4D4qZmmSaqigxroL3r4YcfxqVLlyQ8g2qUdnpQ9ZmdnZWYNxqZtX2JaDOTyeDI\nkSNCiAwVmJmZgd1ulwR0JmszhGJiYgKvvfYa/uVf/gWzs7NSYdjv92NsbAyrq6s4deoUDh48iEwm\ng9OnT6PRaMDlcmFubg7f//73BdlStQHuFSzg+RNERd1uVxABUVMkEhHHAYNtx8fHcfbsWczOzqJY\nLMLn8+Hhhx9GJpPBzs4Oer0e5ufnEYlEkEgkhuoCFotFUZ0CgQCi0SjsdruojDSmA5AzIojWOZ9a\nTaKEZngEvZBa9QoGg8hkMlhbW0OxWBQVant7G8ViUc7BBe5ltFgsFgmhGQwGEnTLEAY6QhqNBlKp\nlNyv4824FvSuMzwFgBy9SUHNjBoyOovFIs43enXD4TAikYgIVRZRYA4wUWE4HJZ0Nx5vyfxiptgx\n9pTOMqrPOg5Pm4K4n3hAEQvH0gHCk9reeOMNTE9PS+kuCgatVgL3GJ0OoNbhYyzMwWKtLOtFxxyL\nWlCgkTZJXxSGjPXk++iEpAAjaPqo9kAP3KE9gwiOhE3kxQ1BREZpzJZMJuF0OpHNZlEul3HgwAE0\nm01JVN/c3JQ0KDotqAq7XC6EQiFhcvTIabjPOmpEI5/+9KelivLGxgYmJycxOzuLRCIBn8+HRCKB\nN998U+wRuobd8ePHMT09jfHxcZRKJblOpMECAbOzs6ImkXCoptCmR5WVDJBQH4CgLn7qwFZuFgBi\n++AhQhQ2VHkZ1sGQHSZZ0/APQOrcHT16VNA5g7jJdHhcI73uDP+hjY50wOh+pstZrdahJHCqxQxv\noSq4vr6ObDaLTCYjwo4MQ69lpVIZmjPmM/OgbW3L4mllRBPValU0ESLOmZkZsUkyNq7X60nJeZbm\nX19fFzNBo9EQpuRyubC8vCy0TvqnKuh2u2XjLy4uIhKJSIgKUU+n05H0tsnJSWEOAEQ7otBijUAy\nV3o3GdcJ7GoptMfSlEEmqMNaqArTBMX4S51yxr57vV4J1iYDpCBmCA5T/gh6OMZisSjo3GaziUed\ndlfa1nUZro9qD4zZUe3jopAoadgkI2ACvmZ09Nw6HLsndPEgZm4gejxPnTqFn/70pyJhiCK4GXgW\nAdUoHdQYjUbFzkXV9OjRo2IU/+EPfziUEE8j/SOPPIJ//ud/lnJIRCpMRWIsmI7bo2ueY+N9XEDN\njJg9wMBMEheFBDBcb4zXeIA07UlEXdpwzUPAl5eXBYFwPplzOxgMsL6+Ls6ZVCqFXC4naU6Mb6OQ\n0rYUXufG1rm93Ai06xCxcw5arRZqtZo4KBwOh5yCxiwHlgLy+Xxim2UqFZlqt9sV1G3G1jF3lJk0\nrNHHTU2NgLnDFNYMJ2FoB7OC2E9mxTCFjQ6mdrstZcloA6Q9jOYaVmE+ePCglL1izcNMJoNCoYBE\nIiEmCJ25QxMAA5F11gftZgxyZt60Dtsi3fHZWghw35Lp6ThZjpsCn+E1vV4PiURiKESHRSOouY2P\nj0uIENG83W4XAUxGB9wLltb0/lHtgTG7RCIhi8b4KzKhZrM5ZB8g8qM3hhCYhEeGR7Unl8sJsaVS\nKSwuLqLX60k6FKU1czk7nY6cwbC8vIypqSnJ5SSRMwQgEAjg4sWLQrDZbFbOUqAE/Pa3v40zZ84g\nnU7L4jH0gbm32rvMnMZ6vY7JyUkZL4UA47Q8Ho+o5FRNyDiowgWDQREGnCvaoVh5RVdOqVarqFQq\nyOVyKBaLWFpaEobBfmrb0tbWFlZWVlAulzE+Po4DBw5Irma5XJay8qw5pkulk2mRUdPQTqHGT641\n54xrwEwUAHK8Zb/fF6cMNxHjCnu9exV+WeSAtNbtduUYRqr7DPClek7PMQViv9+X3OF4PC7nahBp\n0uzCEk3accPQGZa3b7fb8Pv94jVlsDO99HQMLC8vS5HUxcVF2Gw2yTyy2WySOpjJZIZyfhmHx785\nLo6FzItlorgmZLr0OvOsZNrHaaKhSs/YQSI/Zhk5nU4pukr7JYscMIau0WiIsKXAZyEDIjcyxGq1\nKhoY+9HtdkUw0UxC+hjVHhizm5iYEMmjgx0ZMMpNyU2gjY9MjGaJbkbw5/N5IXBmRFgsFnzqU58S\nY2+hUMCNGzcwOTkplYvX19fFmP74449jfn4e8/PzOHTokMB0Splut4uHH34YP/jBD9Dv97G1tSU2\nGEqcdrst9gWdfkQESJWam5O2Bqo+RLButxvFYhGRSATRaBQHDx4Uw7neoGtrawLvSdRkGMC9xHTO\nk1Yxl5aWEI1GUS6XsbGxIcggHA7D6/UiGo0in88Ls2OGx8GDB+UwbAbo8pN2JnrKAYgzhY4Iqui8\npiudABA7GJFgs9lEqVRCo9HA8vKyeOKpHpNp8VlknMzdJdqqVCoyV6xGQ0TFIgD6HOFMJiMJ81wT\n2kSB3Tp+Y2Nj4jSgOpzNZgWl2Ww2VKtVcQzoQga0qbHUEWmCWRfJZFJS75j4TibHIhQ8m5gCmKop\nBWGr1cLOzo4IQwb81mo1KcnFytnUbujAYHl9algazQ4GAwnBoaqshTGdVOFwWOpR0lRCLcXv9wuD\nouORdSMp8IhC+Y9on/3Ve+mj2gNjdjzwhaWMuMlo5KSKC9zz+nAB6dK+dOkSnnzySbhcLmSzWUxM\nTIjzgAswMTEhye48ts/tdksGg8PhEJsb1a25uTm8/PLLsFqtSCQSYuAmukulUpLrurCwgJMnT4oN\njkZqhkdQTdF1t2hLYmu1WhL/xjQ3jjudTsPpdCKVSuHIkSOyqESErGvW6XREunH+tJQnY+QhOzs7\nO7h79y7GxsawubkpnlWeg8tzWQuFApaXl9Hv75a+pw0uGo1K9gqzLJhorjNKtAOKoTEkWMZcUb0i\ngyEhU0BUq1WUy2XkcjkJjF5cXJSkeaqZDFnimcOkBTJOqmc8K4MOgO3tbUG7ZHREDl6vF8ViUaq4\n0ITAuD/GmxUKBVGxqKXQtkm7GZEg1WuqX9wDfr8f0Wh0iNYOHjwoArJSqWB+fl4859QSaFcrl8uw\n2WwIh8MSrM85pBOBZdIYIpPL5TA3NyflkygQaeum4OLhSUzX0vb1QCAw5MwjGtPMm9kwuVxOkN/k\n5KSkwSUSCeRyOWH60WhUMlz4XqJANgIjzuP/szY7phzVajXR+0mgVHeowuqARDYaPN944w0xtno8\nHjz33HNyPqvb7cbhw4fFTsFNz7inf/iHf8Czzz6LQ4cOCTNiACSTtx9++GEp3shSRIwQr9frUnjx\n5MmTQ8Z2ql9UxzUC4WamZPzRj36EVColmwfYRa+FQgHdblfyJoH/y9y5xjaan9f9kKJE3UhRJEVS\nlEjdZ0Zz0Vx2Z3Z2N3vJ2hvb2cRJDDTxuqndGG2BFsgXF0iLoA3cokDSAv3QBuiXNikcFy1SIC0c\nNLXh2G7W6/V617NzH82M7hfeRVKiqCtJif1A/5555XjXgItiQmBhj0Yjke/7f5/Lec45j5TJZKza\nhRSbzWYt40qyww9uRXuVz+eNH1WpVHTjxg0DzJPJpJFeIYseHh5qaWlJS0tL9sBEo1EFg0E1m009\nfPjQWkEGGtiuE0j29/ctUNCuSDLqhCRrEfncDCVgz+/t7en27dv69re/bdM8qvT9/X3F43Hbpety\nuWwAxMPQaDTU398vn89nSYEqHUoMwDkDIKbMUqtSL5VKikQihh1Wq1UjS9MmAqJzX5Ecwh1Dq4wJ\nKTK7vb09JRIJxWIx9fb26v79+5ZUMY2o1WrKZrPq6OiwhEgbjFHC+vq6VY5UbwwkSIBU/FS+8Xjc\nhgJYrtE+lstlBYNBdXZ2GgTi7Cqc7tj1et3aefapOF1tgsGg+dnBA6TqBgJBIcPzQ3fEGYOA7VR1\nOO3PftrrqQU7r9crv99vG+adeBw9OZgNAK5TV0llQDvE5Od//+//rd/4jd/QyMjICcItDxSZd21t\nTR6PR9/97nfNKYUpXbPZ1O/93u/pM5/5jOFCtCjsN8XXDvIko3VoMGQ2hi75fN5oKZAzmXrNzMyo\nUCgoFAqpUCioWCzq+PjYWszx8XE1m0299dZbBq57PB7duXNHwWBQ9+7d0/T0tAYHB5XL5XR8fKz5\n+XnV63V95jOfUTKZNHUFu2V9Pp/piEdHR23aB5Uhl8tpcXFRuVxOHo/HhhXr6+t69OiR6vXWYh5J\n5sCyt7eny5cvK5FIGD0oEoloaGjI5HMkLx48pntUvLQ08K2++tWv6u7du0ZFcLtbpp3BYNA20tHu\n0WrTWtHaeb1eS1JMw2u1mlWopVLJJpE9PT3KZrPmPFKvt1ZCMqji93FOw+GwDY2Y1q6srJg5KAT2\nzs7OEzQLrifsgdHRUeO9uVwtjz92FkMQrlarGhoaUm9vr9F0wHrRe/MepCeDKlrBXC53wjmGTunw\n8NCI8T09PTbQYHESyQp4yEkt4nfUajUFg8ETNk1U23Q6vFZXV0/ANWCsJGtJFvxwViZIA9UgK+P5\nl2Qwyoe9nqpcjElNIpFQo9EwJ1knjgWfiADHzXOC2Xw/1t1/+Zd/qb/9t/+2tcFUC2QjMtTLL7+s\n//yf/7NVEh6PxzAaj8ejz3/+8/rqV7+qR48eKRAIaHh42Kaqn/3sZ3X79m11d3crFAoZEO9yufT5\nz39eX/nKV7SxsWE3Cc0un7Ner9vmqGazqbGxMcXjcUmtg3Lz5k1dv35dU1NTtvawo6NDo6Ojpot9\n8803VSgU9Gd/9me6ePGiybnAs6icNzc3tby8bO0R08lgMGgbwVAfbG1taXd3Vz/4wQ8MN6LiCwQC\nWl1dVaFQUCKR0Isvvqju7m7l83ltbm7achcUFzMzM+rt7dX6+rpJ+mKxmCUvrg10C5LK7u6uvva1\nr+m//Jf/YhVXR0eH4UGw//f399XT02Mb7PCJ43yAB3d0dCiRSEiS4brRaNSqEYjTnCF+J1NLiMwd\nHR3y+XwmM0RfK7UCC3tGwNHw7yNYVSoVraysWPDzer0qFAoKh8M2qd7Y2LDhAbZNpVJJ6XTaLOfB\n7UjiGHsSQAnwDKaoCuk8GCpQ2ZJkdnd3LaDRFhJUSM602uDq3Bcntiq1tLsQ+TmHBPdQKGRDEdpT\nBmV0VgRuqnsw4XA4bPeFlpmk1Wg0PtLT7qny7MAXnJE7m81aBgVg5c9cBDIK1R1YDRfl/fff16uv\nvqqRkRGFQiGz0qFMR4RdqVSMvgA+A78nFAppcnJSu7u7mpubM4kWE8Tp6Wl95jOf0f3793V0dGTS\nJUm2/g9zUTZFXbp0ydqNjo4OE+xfunRJ58+fNyyv0WhoeHhYFy5cONG6v/7669YewRs7Pj42DSyB\nqLu7WyMjI0qlUvo//+f/WGXp8/lsoxr6QrfbbV5r+Xxe3/nOd4zU7fV6df78eZ06dcp4XPF4XIVC\nwSaDLpdLsVjM2hlA+YWFBd26dUvlclkjIyNKJpM2UQU3hYfY1tamVCplQvd/82/+jba2tqy1YgJH\ntcy9JhDjCciCGr6XaaEkra2tKZlMGsF1fX3dCOdU4FRotLlMnAuFgiUJAlepVDKsEHMCKBPgv2BZ\nh4eHBnf09PQoFAopFArZJD8cDqu9vd3eVyQSMTwNNYWTqgRQXy6X7T6hFEEeiOYXeIZz6dTT0tKD\nIXZ1dRlnleCDowzYbKVSseKDAAP0xH3o6OiwrXhwBo+Pj7W1tSWXy2VTWZfLZdg6359Opw3jZhAp\nySblBGAwVWAVuoWPej21YEfp6fF4bKLq1MRxqHAGceJ2TF84yDxoVHput1v/9b/+V33iE58w3Sw4\nFz8jHA5b27O8vKzJyUn7mbQqbJyi6iCbwAtaXFw0Pz7G3kdHR9rc3NTly5clSe+++67a2lq7MO7e\nvasvfOEL5hyCceT58+f/2mLosbExw0ZYL0gg5P3xQPPnjY0NZbPZE3It2t6ZmRnDonK5nA4ODqzy\nrFarWltb0/Hxsc6cOWPysGw2a0GgXC7bAvD+/n6jo/DA9PX1WTvI8p2trS3lcjkVCgWrBiORiJkz\nQBgloD18+FD/6T/9pxM6SATxeJ1RBVKZbm9vGzwQCARULpftoQI0l6RwOGwcMDTKtG1Sq+JzBp1S\nqWSUIpY1cfaazaZN2D0ej4rFok15oXzAMyNRk3DBSBmiEWwODg7MyYQzL+mEGqetrbUSsqenx2AR\nJy5GIKQypAWFW7i/v69isWiJhkraWT1xzkj83B+oQwSbHx+UgYsS3EkgtPLg8JubmwZR7e7umnSu\nvb1dpVJJbndLg93e3tolzMSdvSxOhxe3221VJXHgo15P1bxTkh0caAxMgsDBCHpOlrmzqpOemApA\n/t3f3zdNX1dXl1555ZUTkyGptTeVIMXKwVAoZAER2sPQ0JDW19eNOsB7lp7QEGgD3G633nvvPaVS\nKc3Nzcntduuzn/2sXnnlFZXLZf38z/+8Lly4IEm2PObll1+WJLvxAMy1Ws04XTw0LLthXwHDndXV\nVZv4EfA7OjpMHeDz+XTz5k1jpKOCQKmBV9vU1JRx646PjzUyMmKBCiA/FAoZSE4VACANPgrdhoeF\nxJDP5xWPx3X16lW1tbXZdJzhyb/7d//Orj9TUxLa8fGxYrGYXZdQKGQVTqPRsAAyPDxsy2WcFAWG\nPDijzMzM2M+q1WqamJiwFpffC1Wlo6PDQHUeMKpYp/Mv750gxiCIzoEBBrQcuJzY9dN+4iICdae9\nvd1aedpvKFFAAnwfv393d1fRaNQGXLSvOzs7dtawUHK73RaMSSTATJVKRf39/QYrHR4eqlgs2sCH\nZAPNhy6N+85ZwpWbCTXXATyRFnp6elqxWMyed1QnHo9H+XzeYgPXEwyUYPxRr6da2fFw05+zrg5c\nCwE3h9ppz0Pm44Jyo3FnxSL9n/2zf6Z/8A/+gV588UWNjIwYSI6qgakOh41gwSieQQqVwfHxsR0W\nluqsr69rbm5O9+7dU2dnp65fv65YLKaxsTGdP39ezWbT9szickKAhGAJ9ri5uamFhQWrbhuNhvL5\nvJaWlvTuu++e0DGS8bC04r1SNRJ4UqmUfR7wjVOnThnGk81mrS1zDnV6enqUSCT08OFDlctlbW5u\nmp6RF8RscFNs0kkCXMO+vj6Vy2VlMhndv39fr776quLxuBKJhIrFov70T/9UAwMD1qamUin5/X5J\nsuEEGBMVBX+m8iCIg0khqarValpeXrYdo36/X9lsVoODg6pUKjZEamtrM8max+OxxAD9QpLhveVy\n2YYuEOOPjo4sMBEAM5mMTVSxpbpz546azaZisZhNS1mw1N3dbbgUOJvP57M2T5K5q2Am4OQu0v7C\nK+TZQBEDBri1tWXvg8AxODhoS2ugzjCZ5z4zeaVNh2JFRYeOlmoUChTPMueL6+0saGj76SRYeg6j\nAh25UwlE1e+ELD7s9VQxO6ZxvFF2uDr5PgQjSmjaDqo6fo7H4zG/MQIkjhP/8T/+R33nO9/R5z73\nOZ09e9bG6ZOTk4brEUiprDicx8fHOnXqlJLJpGk0JdmKxlKppIWFBW1tbenSpUuamJiQ1+vVCy+8\nYCAzN5zs9b3vfU+rq6tWDXZ2dtpEGr0n2YuBTa1W09DQkK5evWoSGrfbbWaRTLzQvjon1RcuXNDh\n4aFWVlYUjUY1MjKiYDBoQD/ZFeoAh4+/DwaD9jn29/ctoRAcCb4ul0sPHjxQNBo1D0EnyZvr0dfX\np3fffVfxeFyZTEaPHz+2iptl2iyspqKiUlpfX1dfX5+1fGBC0DnAlagoaPUJSk44hGkqFQQVHBNh\n8DfUK2BItJTcf3zySJRoTxl0wB+FB4l6h7aU6Sh7KaB/7O3tGQQDt61QKNhOEnTbnFn2zPI5wPJ4\nxkiQDDVoe6mwCoWCTWMZZgGbQBqGHUEXwX1zBk1noAVO6OrqMjggm80aPMQmN85XtVo1SKCj53tM\nHAAAIABJREFUo8OGf9Vq1SrvQCCgqakpSwgYhPBsftjrqQU76WQrK8mE3NJJZ2JATIIfpFuCAZWZ\nJAsc4DYMNm7duqV3331XwWBQf+fv/B29+OKL8nq9+lt/628pHA6b/TcANdOfZ555RolEQj09PVpZ\nWdHdu3d1584d4znxUEGQfPDggb73ve/ZoeN9MU2GqQ6PkMPBofL5fOrv79e5c+fU29trQaVWq+nK\nlSu2BY0Dfnh4qOHhYd2+fdskYSSIiYkJ003u7OwoEokon88bLkobBYb2l3/5l1Y5ck/AXqDD9Pb2\nntiPcHx8rKWlJUsayWRSCwsLCoVCGhsb04MHD+T3+1UqlTQ1NaXd3V0NDw9rbm5OkrTyow1oiLxd\nLpftmAgGg8pms2ZGCRjO+WDQ1NHRoXQ6bUG2o6NDCwsLCofDNlXFoRlO4ksvvaRqtapKpSKPx2NJ\n1uPx2M4QHl7smVD1BAIBhcNhSTI+GB3D0dGRMpmMka+lJ8M4py6Vhxw6yvHxsXw+n3HOkE0h55Ja\nO1fK5bJJLZeWlqzd9nhaS5o2NjZ09uzZE2RjYBDuJVN3fOSoSuEFDgwMKBqNnqCgYPHEvYKVACcO\n3JQKDwUJnVehUDDpJMRvpGROfTGuOZFIxPwYaV+ZOI+Pj1ul7fP5VCqVjNf6Ua+nuiRbkh0AXk5+\nlrONY/RNliVQ8HOokhhxl0olk+6w+5XhwR/+4R/qW9/6lo6PjzX6I3snrNURvVMlBAIBZTIZZbNZ\n3b1718i8h4eHloVyuZwdSMp8Smpny+3kBMLLcg4mvF6vzpw5o+HhYQ0ODppfG27AP27tBLgciURs\nPSMSnIGBAcXj8RPeejwkuFBITwBwSXr22Wf1wQcfnJA8UREQZPL5vC0EevbZZ63aqVarCoVChuWw\nHwMSLTbqVGcsuvZ4PFpbWzMAvb+/30xDqdLApKj4UQrAO4M+wvvkGlD1U33A1Ad6cIL7GETW63Ub\nfGDcCs6FwzJVHfsS4MehrJBk0AKJDIMAJqQ4+y4sLFhAJrCh8cbqCswTrh7Xg0CRTqft5/p8Pls4\nxKAEOKBSqWh5ednuB+eOytbtdpsaSdIJV+a2tpbPIbw8sGOCDEUGrTJVP3pzgtzBwYFhtdjWU6Xu\n7+9raWlJfX19RsYmeUMPY+k3U2omyUAK6XT6Q2POTw12X/ziF/UXf/EXikQiunfvnqTW5Oo3fuM3\ntLq6qtHRUf33//7frSL7/d//ff3xH/+x2tra9O///b/XL/zCL/zEn7uzs2M2OwQ7qh+UBJB0Ibo6\nZSgcKg4zB4hgRPWxtrZmba7b7bZD9+jRI0ktQTkZipvebDbNhoYHguyMVpAb5qzQCGZOtYb0ZPLs\nFGHjZef3+606bWtrbX9vb29XPp83hvje3p6uXLliFZ2z2oOy4/f7baculB5J1o5QCUYiET169Mh0\nsJFIxDAf9MRw7fj3x8fHeuGFFxQMBnXr1i2trq7q1KlT1oZxH3Z2dgyf6u/vV6VS0dDQkNxutx4/\nfmy0nv39fdsLAhWFySgT6lwud8IuCtoHWOT+/r66urq0vr5uk0MCFq419Xr9hC2QUx1Qq9U0OTlp\n6gRaNWgt+/v7SiQSFqCpdqlAMpmMye1IPru7u7YCEXoFZ7JWq+nBgwdm/En3UCwWtb29rfHxcTtb\nDBOQmElPhmHQYsDySLZQfph88jmgr3Adjo+PDcLg/Do5reBtjx49MqoOAZ/PiJ41k8lYC06wp6Xs\n7+/XxsaG/X6GIODlELu7uro0PDxsZx0TBFQ6TMl5hhuNhk1tkUrSDTlpWj/p5Wr+lHnt22+/rd7e\nXn3+85+3YPc7v/M7CofD+p3f+R3963/9r7W5uak/+IM/0OzsrD73uc/phz/8odLptD7+8Y/bVPLE\nL3W59MlPftIeCloRbgKtCC1COp1WNps1XAZcgTJ9a2vLHgZ2QPT29iqfz6tYLOq5556zadzDhw81\nNzdn1QuYFX/m4NJSwewHd5FkkiGqMkkGZjtxSKoiwOOuri5zV3H6vEFA5bPx4DcaDeMqTU1N2cMj\nyVpkrJZqtZotc757964FUoi4TqPPVCplygiuPbhgLpcz7ANe1uuvv274GK0ObSLcOKof7iGfBcxs\nfX3dcBkGI9wzAH8qvlqtpkePHllV4VzcAueMf9/Z2Wm+hJIMFoDEzBq+2dlZw3KhSFB9EWhoB5HZ\nkfjy+byRp8fGxnTt2jWr3Ofm5jQxMWGV7NzcnJm5EkSlVjW+srKioaEhC3I/jltPTExYK+j0tOPh\n5h44BfePHj2y68M2NwIPNlYPHjywVpagglMNuCxBj0kv1lLAByQ15zCnUqloYGBAR0dHprEmSONU\njBSU+4bEzmm+C/Xs8PBQW1tbisfjRllBoUTyhZAtyZIS3+/xePQ//+f//FAKyk+t7F566SWt/GhZ\nNK8///M/11tvvSVJ+sIXvqBXX31Vf/AHf6Cvfe1revPNN9Xe3q7R0VFNTk7q/fff1/Xr1//azyUD\nl8tl43jBqIa0SBDiMDhlM4DX4A5tbW1Kp9OG6W1ubmpvb0+XLl2yQNfT06Pz588rFArpvffes6oI\n7hBj/B+XrUk6UeE5SY8cIAKBk5MXi8Usk9frdZ0+fVqSlEql1N/fr0AgYLSXcDhsB+f4+Fi5XE6h\nUMjsdZxmigxzwH7cbrcmJyetIj4+Pta9e/eM29TX12dtPhgKrhalUsnuhySTRtHqAgRT2Xi9XiPa\nlkolC3b4lTmHLmR0VAjs53XaTLlcLq2vr1t1cXBwoHK5bDZT2AdRQVNFOCs5OFoA9pJOvGcqWqaz\nm5ub6urqMq0wRGOSAsOgpaUl4+9tb28rmUzqypUrcrvdRkjmLE5MTNhWutXVVbOw93g82tra0r17\n9zQ2NmbBfWhoyCyO2KMxOztrMjGSnpNaw3sFvsDMFe4eGDLwzvb2thYXF40kToXf29t7wg+PFZ1U\nTtVq1agtnBmcpGl3mY7v7u7aMAmM3dmBYOkEzs5kG0cT4AeGJAMDA/YsITdrNptGoyHwcw4Y+vD9\nH/X6mTC7fD6vaDQqqeVLx77LTCZzIrANDw9/ZA8tyfpuj8djZEQCiZMsyEXk/6+vr9tNnZiYsIeT\nBdmZTEYDAwPWTjnF4xgVwh+q1WrG00KsT7bjvdBqkm3BsDo7OzUwMKCpqSl76I6OjrS8vHxia3xv\nb6+GhoaMRjE1NXWCbAqrnuDbbDa1srKiy5cvKxwOW7tGVcKhW1tb0wsvvGAsdY/Ho7Nnz8rlcmlu\nbs4eRvz9nHgQonp+XiwW09DQkNED4PBVq1XFYjFLRF6vV6Ojo7p//765UtBSezwek/1AJapUKopE\nIob9QIDu7++3FYiFQkEej8fAffhsYKMTExMaGxuz3bu0VcjGmMbDvSJhkf35PM6hEMuNGD6USiVT\n3JAgG42GQqGQUqmUYrGYcrmc/V4mkk5MmEQnyQIeels0yVyfjo7WGkhUCUyCSTgI/Gu1mv1b7vPj\nx4+VSqV07tw5tbe3G2xDRbS7u6vZ2dkTkAZnwEmQLxaLJnskCaJV5hrStg4NDdn3SC3FysbGhtmj\nYc3EM9TT06NqtWpSvO3tbWv7y+WyDSZwGybY7u7u2rNYr9dt6bskg3ioHOkAmRB/1Ov/eUDx4wOG\nn/T3P+m1vLwsSRb9EfcSVBhrA/ZDPqWMpvoDkKZKgujIxIc2qFKpGCk3EAjoxRdfVDwe1+HhoW7e\nvCmPx6ORkRGrJJj+7u7umv+/z+dTJBKx1jWfz2t5edlskUKhkAKBgC0NdrLOaVlLpZL9DrAQPp+z\nWpGk6elp2+bl8/lMdnZwcKChoSGVSiXNzMwoHA6feNgajYYpGJhoEVTAdAKBgILBoAqFgqlXmBRz\n2AGrUXLgn8a9cRK9CT4bGxsaHR1VZ2enNjY2lEgkDFvz+XzGUcTjjOpekl0PKhDnEIv3xyADsT2t\nGhUw/DeMKal2UJWQjDg7p06dkiTjtwFPEOSQTlERe71eU7fcv3/fNM1ww/x+v6anp7W/v6/t7W3D\nqVjz2d3dbeoF8GImm0w16/W6GVVQJTMY6u3t1b1791Sv1+1MBYNBU8xIrep8dXXVBjKYWzgpPFRU\n3d3d2t7eNpNSvtbf329DIborHKlJIOzfcCpVqNCAcOjKjo6ONDg4KI/HY9ju4OCgVn7kit3Z2WkD\nC9xn0L3CtiCJEHAjkYjpvp1B+MNeP1Owi0ajyuVyisViymazJsRGbcArlUppaGjoJ/4MhNkcbLKk\nJJue8ff1et0yIRQNMjgVISP77u5uZbNZeb1emxTBKN/Z2dH09LReeeUVJRIJA+29Xq++/vWvWyvJ\ngXCCw1R/w8PDOnPmjAKBgB49eqRoNKpnn33W9I8+n0+5XM64ShgEEIQ2NzfNVse5Fg/cjox7eHio\nycnJE2sHA4GA9vf39ejRI7311lvq6upSKBSyRS600UwBI5GIlpeXFQ6HDdeh3UskEgYEf+c73zHG\nPsAwbcfIyIhWVlbsYYKND0GWg4wsCWrLa6+9psPDQz18+NCUGrVaTbFYTKlUyipc8M/h4WG53W7D\nU2nlAfhJfM1mU/39/ero6FChULCKjQk5CYNMzwMHoZgHlQkenQXKkI2NDZ0+fVoLCwvWcnEvrl27\npmw2q93dXZ07d06SDEMjWNGahUIho0CRCNGzYhUFq6Cvr0+VSsX0onQbBwcH5nOIyP3x48fK5XIm\nX1tZWdHGxoaCwaAVAPfu3dPKyopBNhQPELJxDqKVZBrsHOJBrero6LDugwEMCdk5mCAg8jVoU9h8\n8Tt5dtGaI90rFAra3d017JXKkGIBuyfI/+wv9vv9ikQiWltbUzQaVSqV+tC49TMFu09/+tP6yle+\non/yT/6JvvKVr+hXf/VX7euf+9zn9KUvfUnpdFrz8/O6du3aT/wZXCCARfAvAh0HHE0r+BoXiyCB\nxIbWCEqCz+fTysrKiRbr3LlzunbtmnGhenp6NDg4qPPnz8vr9erdd9/V4eGhYrGYBgYGrJz+xCc+\nocPDQ/3gBz8wzl17e7vOnj1r1YDf71c+nzcxNAfU5XIpGo3awIQDiQ36wcGBRkZGbL8DgR380KnH\nJThgKBqJRLSzs2OedfV63dZKQkrt6ekxLNPv9+vy5csGDkOaRsXglPpAJ4CIS6vrxE2d3m5UoM89\n95xxqvx+v7VpgUDAYATIy/xdvV7X6OioyuWyms2mksmk4vG4stmsLYQGUqDi8/l8ppKp1WpaWFiw\nSrPZbBothATK8uVQKGQJ1jnomJiY0OPHjxWPxy2RZzIZtbW1dlO8+eabNmHESOH06dOanZ21JChJ\n8/PzVsnHYjGrPP1+vw29Njc3NT8/r8HBQUuCtJh8Dx3LwsKCxsbGLHhCKId6Aum2Uqno5s2b8npb\nS5QuXrx4ItkCP4CNkmyZetM5cXaz2axRlwjKdEhgqSQjJG9w9qga+ZlIE8ErCYpM1zs7O5VIJGwY\ntLOzY9p1Jttck8PDQ1uPQGXvdrectaHM/MzB7s0339Rbb72lYrGoRCKhf/kv/6X+6T/9p/r1X/91\n/dEf/ZFRTyTp7Nmz+vVf/3WdPXtWHo9H/+E//IcPLS2dsiewFG4CwY9WiQsHZkSwY7K3t7dnwuhg\nMKihoSGdPn1auVxO3/ve98xGKJVK6Zvf/KYGBwcViURswuQMtrlczhjcly5d0szMjBEgk8mkVldX\nDcfzer0KhUJGpwiHwyqVSiZ25tCj/WRnKMMZpDLwA+Entbe3G8EUjhZyGrBMpG/RaFSFQkErKyvy\n+XxGwATXHB4eVkdHhx4+fGiuIE5qAtNIZ4uHKSTaWnbn0mL19vaqUqkYwTmXy2l9fd3UI07MNBAI\nGE6aSCRMbbC/v2/BLhKJqFqtGtH27NmzOnfunB4/fqy3337bJG5ObhtwBsaQ2AahpqASdlb3Ho/H\nVm2yASybzWpkZERra2umdkD5wVR3bGxMLpdLuVzOupg7d+4Yqfno6IkDMBVoPp83OZoTw2OYMzc3\nZ90Ik3Wv12syLTwC3e6WVTnBrVarGS+NIFYqlVSpVHTt2jUD9TGPABpgWgufb35+3pIG1TmUJpLr\nxsaGVcng2Llczs60y+XS7u6u8vm8DY18Pp+1rAsLCwaZAC8QJIFjJBnPtVZr+Tuyz8Xj8SiXy5n1\nv9OeCvoRid7JZ/2Zg91/+2//7Sd+/Vvf+tZP/Prv/u7v6nd/93d/2o+1i0WUp93gQ1HBSU/s2Mka\nZCdwLz702NiYcbtgjI+NjWn0R+aUt2/fVrlctsAaDoeVTqfNptztdmtwcNAOWiwWs5ZSkvnv7e/v\n24NF0ACzcDLewTJKpZIFGR4mp+8+DxctuiTb9ESVhqaRYEflxwRre3tba2tr9js6Ojp07tw54821\nt7drdXXVsi8Bo1qtWpXsHI7QThGESqWSqTGazZbWl4kkX8O6CmlQIBAwe6T+/n4VCgVrRXK5nP0+\nkgsgO15x+NA999xzFnjArTAYJQl2dnZaWwYVBkwWjJRNW7wHqEI4trS3t9tGNGfVcfXqVZ0+fdqu\nI7pPYIO2tjarAhkgNRoNzc3Nnah8OCe1Wk2RSMQGU9xzBh2lUkkTExPG22xvbze9cTgc1uDgoOGS\nPBu5XO6EKwrwB+dha2tLjx49soERAT8Wi9m9RqnE80ki476AW9KFELgZADLAow3nOvEZvF6vdWzO\n7W98nW5kaGhI8XhcXV1dtieY4SRUJmIHhUOt1lps/vDhww+NOU9VG0v/7Qx69PbOSg9iKQfL6XpA\nxsEXjywK9+7555/X4OCgms2mBgcH9fDhQ925c0cLCws2RW5vb9fk5KR++7d/28brs7OzdmHZBUrZ\nzMCCILu6uqpwOKydnR1ls1kbgnR1ddlhgdLgFE2DYZRKJTsUtBp8HZ0lXKVyuaxLly7ZNUBAju/Y\ngwcPFAwGlUwmDU/BXhy+Ilvmnex4gGFcQNhdwWccHR01s8lms6lnnnnmxGpEfO5SqZQGBgYMG3O7\n3Zqenja6x+HhoXK5nM6ePas7d+7Y4U+lUgoEAiYTAkMcGBgwvzhJRqIdGho6UfVGIhHzY6Oy2djY\nUKPR0NTUlCUTML7t7W1r56kyZ2ZmtLCwoHg8bvZVKEVQUxwetpaiJxIJ28kRiUQ0ODhown0MInZ3\nd7W4uGiqkuPj1h4PJFlU9BMTE/aeK5WKEZ2BPDo7O22KnslkdOXKFeMEVqtVPXjwwEwgGCyAb0pP\n1nPmcjlzVGE6TpvrJCUTdIFVsInnGWQohRszfEmSbL1eVzqdtmEMNCC3261cLmf4JpUhlSNYYTwe\nt4EgHcfGxoadW2SddEmwOcCZP+zV9uUvf/nL/z+C2Ue9/sW/+BcaHx83OYuTWEnrStbnf8l+fEgn\n1wdgmrYROU93d7fi8bgRRDk4m5ubyufz9rtef/11/eZv/qa1bwwMNjc3reWj5N7e3tbc3JwdKCZZ\n2KEHg0ETosMv4r3yYEK8BLiloiOT016CqUG45GYPDg7aKJ6vMXFtNBrGN6OVoaqk5eF6MpV0VqcE\nKEmWeAYHBzU+Pm4+Y6zVg1sH6fT4uOWgAglbkl3H06dP25rGWq1mu30TiYSeffZZSx5YewP2E8Q5\nHwxxEomEieTxPCMRHh+37KkYAJGs3G63IpGIDdUqlYoZtF65csV2aGxubqqnp0cLCwu6ePGiObZg\nZ0TLNz8/bwmxo+PJ8h9aN7fbrWKxKK/XazJEHnRkT253yzwVOZbX61V3d/cJXiG2RpVKxSpEeINL\nS0uq1+uKx+PmjILJANgkyhHuKcmNc8hzBw2J54yhCPcMpxOs3aHLgKUxwT84OLBlUxQwBHuwZ373\n1taWnR3OZCAQsJ/LKse2tjZTdNAtcC/Qv7e3t2t5eVkfFtKeWmUHZuc8PAi9qXAYuxPgeBGA+LeQ\nVimBqUZGRkZMDUDLiyEg7hLhcFjnzp2zzCTJwGsWubABCcxrZ2dHt2/fNpD/tdde0/7+vu7evatw\nOKyRkREdHbX852ZmZlSv11UsFlUsFjU1NaWDgwMtLS1Jkm2Wp7pjzRz0FecgZm+vtZgln8+rVquZ\nJIoqjIEIHCkqQVoTppKIukkgVBgcHLI9UzNY611dXRoaGlK5XDaFg3MZNHZDpVLJuHmXL19WKpVS\nR0eHuX34fD4VCgULEKwiZHpHC9/T02PVMm0fdAeqC5QphULBHi4e8kQioUqlYkx+hhEEVifVaW5u\nzs4T09Xh4WFdvnzZAg4rDbe3t825w+fzaWdnRw8ePLCqKZvNKp1OmyYV2AIPQCp68LJ6va7vfve7\nmpmZOdHakRA7Ojq0tLRklejKyooqlYpmZma0ubl5wgEFaIVgt7Ozo+XlZQukfEZgC9rXYrFo1SbP\nXbFYtMHO0dGR8dqoAHd2dmxg4mxRncTmZrNpfEvYDSwaApZgYRHUKAwBkEq2tbUpHA5bEo3H40qn\n0xaYgUIglH/Y66kFOw4fb5DMwgSSi8UAgykVAYmA53yQ+TngP9ITE85araZyuWxEzNdff90msBBv\nnZNgsszW1pb6+vqsnd3d3dUbb7whr9er//W//pcBpnixra+vKxAIWLBlgspECpE52l8GEV1dXbp1\n65ZOnz5tYO38/LwFgGazadvGWOXobOP29/etdW82m5aRoQhgOcQ1Q4KDHxif+Vd+5Ve0s7Oj7373\nuyeqFvhtmBcMDw+rUCicsHkiWGIdH4vF1NbWpmg0qrW1NXsv7777rsbHx+17qFz9fr/Onz9vFU8q\nlVK9XlcymbSdBYjg4e3B8wqHw6aRZKewJGuFKpWK7fiIRCJaXV3V1atXtbq6qmAwaC0zGG4+n9fr\nr79unnFY+Pv9fuPVLSwsKBaLqaurS8vLy6pUKrY8Z35+3hIAQ4evf/3r6uvrsyTP70blQpsoyQZW\nkmyIwhRbamHD77//vs6cOaNSqWQdDdcOTHZ9fd2qJ6gkFApU9QTUx48fS5IxHAhiXq/XhhyoOcAr\noYjw3gjInLlAIGDPulNrzIKqZDKpR48e2WADHTyqH7oqhjcEW2AhjFH39vbMK/LDXk+tjZ2YmDBs\njgGAUw5EZuLrToY0FAMnHYJgiMyL0paKB+B3ZmZGr776quk6x8bGjLLiZG0zoUKOlc/nNTs7q+vX\nr2twcNA817DKgSbDej+mlbiM7O7uKhAI2I3e3t4+ga+Ak0xOTlqrCP0FKyKpRdp1Ep75+1wuZwu7\nqQi5BrT5+/v7yufzRuylpXa5XFbhDg0NKRqNqq+vz0B3OI5cY657Nps17AYCLbI2t9utV199Vfl8\nXu3t7VpaWrLKCzB9b2/P7KUYFFCdNJtNbWxsKBKJKBwOG+cNnIbPgM8cmBcPM9AFD6jU0lUSEGnr\n8/m8IpGIVZPACm63W5cvX5bL5bIBA9SQvb09ra+v22AJ63YoPkNDQzo+PlY6nbazy8/nPTJNRi8L\nvxEJFMmNa8G1pwvivgBblMvlEzZHaJjT6bTx0YBpkJzBYaTCBNqhK2g2WwajTp4oU20+A8YcJF2w\nOYItgRI4B0gFZx5a2OXlZav++fm02FwznuOdnR2Vy2Wz5+K60G7/jWtjGReDm3FAkY2Ax4FXcMOd\nVQQVH7y08+fPq16vq1Ao6OjoSOl02qQqSJyuX79uDy6Dg2QyqeXlZXNfcLaCY2NjSiQSmp+f19bW\nluFrjUbD5ECzs7MmI6tWqxaAkTJxIJgibm9vn5j6QQ8g63N9cA/O5/MaHx+3BT5gRaw/hOpQqVQ0\nOjpqgYDrygMKt4rpLlXvtWvXNDw8bCambW1tOnv2rJGCpVaQZVcARFpcK/i8xWJR9XpdZ86c0dra\nmmKxmB4+fGi4n9SqSFhOBPgcDAbV1dWlcDhsAQprdHA7Dv69e/fU3t6u06dP2+8dGBhQo9GwhUNU\ny9wLj8dj176rq0vb29tWqV68eNHUJVQts7Ozevnll9XZ2WkWSi6XyypA/uvt7VUmk7EHvaurS5lM\nRu+9955mZma0uLgov99v/nAkdQJaMBg0pURvb6+1k07lgtRiDWxtbRkJl0kyJOxarabp6Wltb29r\nfX3dYJJGo7W4ieQFn845cQdjg18KBg5pf29vz/iAUqtTcipjoCc5pXltbW3GC8R/r7e3V8vLy2aY\ngC0Zypf+/n7VajWNjo5qcXFR0WjUBhbOjWswIaAhEUydEs4Pez21yo69rrRI2PyQ2WF0A8QzDQXg\nlGSr5dgEht9bKBSybVhLS0smtn/uueeMq/ST2uBMJmNgLlktmUyqt7dXiURCo6OjymQy1mIQSNbW\n1myNI4Cx3+83nIMK6+ioZcYI6Rn2OFY1w8PDlgB2d3ftfQeDQUWjUZvOYefEJJTPQRmPwwXj/Vqt\ntZA4l8vZiH5wcNAkTLRVVHkej0fd3d0G4iOYBzvkZ+dyOXs/UEWuXLmiZDKpaDRqcitkQlILfnjl\nlVc0OTmpzc1NRSKRExSRzc1NgzHAgGjjCdjt7e0aGRmxagG7KtYcQi0BT6N64R4cHR2Z6QK4X7FY\nVDweN47X1atXbW0gYLrL1dqkJslA96GhIQv8fX19Gh0dtSA4Pj4uv9+vnZ0d3bx502hN4GFdXV1m\nDtHe3m6VDk4mp0+ftqTJ52JQlk6nbUEQVRlVbDablcfjMbst1DPgndITO7VaraZ0Om0uOE67Kr4X\nmhUaVPwDOQ/AK7S+h4ctB+jj42PTp6MDZjqLIQSJuNFoaGZmRgMDA+rp6THlTzabNYyP8w7Oh/U+\n3dzf2MpOkrViZFpaVw4xdAtetJSU0M1m0yxiJBn2FolErKrp6OjQ9PS0dnd3tb6+btpO6UmVcXjY\nWhK8sLBgvl+I68GkaLNCoZBxxJisZbNZa++c0yFJNrUsl8snqB6YkyLDYcji5CABInOonAtNRkdH\nLcBiS5RMJg3HwATTST+gGsUSp1gsyufzGdGXA0gA6O3t1fPPP6+5uTmTGvEfhOiXX36eXiG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+zs7DQp2Pj4uOGvIyMjNn10mnqCa/KzecAgH4MRzs/Pm8YXmgnVf2dnp2ZmZnTnzh3b2iU98XRj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\n7Wsf+uy9vpfZ2dng/fffD4IgCFZ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},
{
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GoZivU9hRQFk0snDBToGYwd4OJoxFZTmytVCyA9Q8OGs73zmFCzZgyoJ0Yits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wIwAjdNDQOsPi1hCxR1bw1v+233gECMoEfxvnHc3Psz10iLW7zzB5spXUYA3LzWH0elgkQhWL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\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}
],
"prompt_number": 71
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multi-panel figures\n",
"\n",
"As we noted, Matplotlib is behind the scenes keeping track of what your current figure is. This is often convenient, but in some cases you want to keep explicit control of what figure you're working on. For this, we will have to make a distinction between **Figure** and **Axes** objects."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 'Figure' objects are returned by the plt.figure() command\n",
"fig = plt.figure(figsize=(7,5))\n",
"print type(fig)\n",
"\n",
"# Axes objects are the /actual/ plots within the figure\n",
"# Create them using the add_axes() method of the figure object\n",
"# The input coordinates are relative (left, bottom, width, height)\n",
"ax0 = fig.add_axes([0.1,0.1,0.4,0.7], xlabel='The X Axis')\n",
"ax1 = fig.add_axes([0.2,0.2,0.5,0.2], axisbg='gray')\n",
"ax2 = fig.add_axes([0.4,0.5,0.4,0.4], projection='polar')\n",
"print type(ax0), type(ax1), type(ax2)\n",
"\n",
"# This allows you to execute functions like savefig() directly on the figure object\n",
"# This resolves Matplotlib's confusion of what the current figure is, when using plt.savefig()\n",
"fig.savefig('fig.png')\n",
"\n",
"# It also allows you to add text to the figure as a whole, across the different axes objects\n",
"fig.text(0.5, 0.5, 'splatter', color='r')\n",
"\n",
"# The overall figure title can be set separate from the individual plot titles\n",
"fig.suptitle('What a mess', size=18)\n",
"\n",
"# show() is actually a figure method as well\n",
"# It just gets 'forwarded' to what is thought to be the current figure if you use plt.show()\n",
"fig.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<class 'matplotlib.figure.Figure'>\n",
"<class 'matplotlib.axes.Axes'>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" <class 'matplotlib.axes.Axes'> <class 'matplotlib.projections.polar.PolarAxes'>\n"
]
},
{
"output_type": "display_data",
"png": 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BQCAAIYQ5o5MGV1fXBmO4BAcHw8rKCj169MCOHTvqPc/NzYWnpye+++47JCQk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\nAABJRU5ErkJggg==\n"
}
],
"prompt_number": 73
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a full list of the Figure methods and options, go [here](http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create a new figure\n",
"fig = plt.figure(figsize=(15,10))\n",
"\n",
"# As we saw, many of the axes properties can already be set at their creation\n",
"ax0 = fig.add_axes([0.,0.,0.25,0.25], xticks=(0.1,0.5,0.9), xticklabels=('one','thro','twee'))\n",
"ax1 = fig.add_axes([0.3,0.,0.25,0.25], xscale='log', ylim=(0,0.5))\n",
"ax2 = fig.add_axes([0.6,0.,0.25,0.25])\n",
"\n",
"# Once you have the axes object though, there are further methods available\n",
"# This includes many of the top-level pyplot functions\n",
"# If you use for instance plt.plot(), Matplotlib is actually 'forwarding' this\n",
"# to an Axes.plot() call on the current Axes object\n",
"R.sort()\n",
"G.sort()\n",
"B.sort()\n",
"ax2.plot(R, color='r', linestyle='-', marker='None') # plot directly to an Axes object of choice\n",
"plt.plot(G, color='g', linestyle='-', marker='None') # plt.plot() just plots to the last created Axes object\n",
"ax2.plot(B, color='b', linestyle='-', marker='None')\n",
"\n",
"# Other top-level pyplot functions are simply renamed to 'set_' functions here\n",
"ax1.set_xticks([])\n",
"plt.yticks([])\n",
"\n",
"# Show the figure\n",
"fig.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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3R+Rc2idZRERERESc7+xZa0r1K69YU6p79YLwcGtpZCf49cSvrDu0jkGrBpGY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\niIiIiIiIyFX/D4SJGATQGg4YAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 74
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The full methods and options of Axes can be found [here](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clearly, when making a multi-panel figure, we are actually creating a single **Figure** object with multiple **Axes** objects attached to it. Having to set the Axes sizes manually is annoying though. Luckily, the **subplot()** method can handle much of this automatically."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create a new figure\n",
"fig = plt.figure(figsize=(15,5))\n",
"\n",
"# Specify the LAYOUT of the subplots (rows,columns)\n",
"# as well as the CURRENT Axes you want to work on\n",
"ax0 = fig.add_subplot(231)\n",
"\n",
"# Equivalent top-level call on the current figure\n",
"# It is also possible to create several subplots at once using plt.subplots()\n",
"ax1 = plt.subplot(232) \n",
"\n",
"# Optional arguments are similar to those of add_axes()\n",
"ax2 = fig.add_subplot(233, title='three') \n",
"\n",
"# We can use these Axes object as before\n",
"ax3 = fig.add_subplot(234)\n",
"ax3.plot(R, 'r-') \n",
"ax3.set_xticks([])\n",
"ax3.set_yticks([])\n",
"\n",
"# We skipped the fifth subplot, and create only the 6th\n",
"ax5 = fig.add_subplot(236, projection='polar')\n",
"\n",
"# We can adjust the spacings afterwards\n",
"fig.subplots_adjust(hspace=0.4)\n",
"\n",
"# And even make room in the figure for a plot that doesn't fit the grid\n",
"fig.subplots_adjust(right=0.5)\n",
"ax6 = fig.add_axes([0.55,0.1,0.3,0.8])\n",
"\n",
"# Show the figure\n",
"fig.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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dEoO5Q1LJ9dzK9biIiAwl9Xqo97E0pVKJqKgoDBs2DBqNBlOmTIG3tzdWr14NAJg2bZq0\naEn2mDskFXOHiIiIpNJ758aoO+Jfo2TL1OeWuSNfzB2SSq7nVq7HRURkKKnXw1oX8SQiIiIiIrIE\nLG6IiIiIiEgWWNwQEREREZEssLghIiIiIiJZYHFDRERERESywOKGiIiIiIhkgcUNERERERHJAosb\nIiIiIiKSBRY3REREREQkCyxuiIiIiIhIFljcEBERERGRLLC4ISIiIiIiWWBxQ0REREREssDihoiI\niIiIZEFUcZOQkAAvLy94eHhg2bJlVd7fsmUL/Pz80LNnTzzxxBM4ffq00QMly8TcISmYN0RERCSF\nQhAEQV8DjUYDT09P7NmzB87OzggKCkJ0dDS8vb11bY4cOQIfHx+0bNkSCQkJiIyMxNGjRyvvSKFA\nLbsiC1XTuWXuUG2qO7fGypua+id5kOu5letxEREZSur1sNY7N0lJSXB3d4ebmxtsbGwQHh6O2NjY\nSm1CQkLQsmVLAEBwcDBycnIMDoTkh7lDUjBviIiISKpaixu1Wg1XV1fdaxcXF6jV6hrbr1u3DiNH\njjROdGTRmDskBfOGiIiIpFLW1kChUIjubN++fVi/fj0OHTpU7fuRkZG6r0NDQxEaGiq6b2o4VCoV\nVCpVre2YO/QoMbljzLwBmDtyIfa6Q0REjVutxY2zszOys7N1r7Ozs+Hi4lKl3enTpzF16lQkJCSg\ndevW1fb18IcMslyPfkBcuHBhte2YO/QoMbljzLwBmDtyIfa6Q0REjVutj6UFBgYiLS0NmZmZKC0t\nRUxMDMLCwiq1ycrKwvPPP49vv/0W7u7uJguWLAtzh6Rg3hAREZFUtd65USqViIqKwrBhw6DRaDBl\nyhR4e3tj9erVAIBp06Zh0aJFuH37NqZPnw4AsLGxQVJSkmkjpwaPuUNSMG+IiIhIqlqngjbajji9\npWyZ+twyd+SLuUNSyfXcyvW4iIgMZbKpoImIiIiIiCwBixsiIiIiIpIFFjdERERERCQLLG6IiIiI\niEgWWNwQEREREZEssLghIiIiIiJZYHFDRERERESywOKGiIiIiIhkgcUNERERERHJAosbIiIiIiKS\nBRY3REREREQkCyxuiIiIiIhIFljcEBERERGRLLC4ISIiqqOEhAR4eXnBw8MDy5Ytq7aNSqVCQEAA\nfH19ERoaWr8BEhE1ErUWN2Iu2LNnz4aHhwf8/Pxw8uRJowcphkqlstj+LTl2fZg7pu/fkmPXh7lj\n+efWXLljDhqNBrNmzUJCQgLOnTuH6OhonD9/vlKb/Px8zJw5Ezt27MDZs2exdetWM0VLRCRveosb\nMRfsXbt24dKlS0hLS8OaNWswffp0kwZcE0v+RW3JsdeEuVM//Vty7DVh7pi+bzn035AkJSXB3d0d\nbm5usLGxQXh4OGJjYyu1+e677zB27Fi4uLgAANq1a2eOUImIZE9vcSPmgh0XF4dJkyYBAIKDg5Gf\nn4/c3FzTRUwWgblDUjF3yNKo1Wq4urrqXru4uECtVldqk5aWhlu3bmHQoEEIDAzEN998U99hEhE1\nCkp9b1Z3wU5MTKy1TU5ODhwcHIwcKlkS5g5JxdwhS6NQKGptU1ZWhhMnTmDv3r0oKipCSEgI+vbt\nCw8PjyptIyMjdV+HhoZyfA4RNQoqlcood/31FjdiLtgAIAiCqO3E9ifVwoULLbZ/S469Osyd+uvf\nkmOvDnOnfvqWQ/8NhbOzM7Kzs3Wvs7OzdY+fPeDq6op27drBzs4OdnZ2GDBgAFJSUmotboiIGotH\n/5gj9XeI3uJGzAX70TY5OTlwdnau0tejH0RI3pg7JBVzhyxNYGAg0tLSkJmZCScnJ8TExCA6OrpS\nm9GjR2PWrFnQaDQoKSlBYmIi5s6da6aIiYjkS++Ym4cv2KWlpYiJiUFYWFilNmFhYdi8eTMA4OjR\no2jVqhUfDSHmDknG3CFLo1QqERUVhWHDhsHHxwcTJkyAt7c3Vq9ejdWrVwMAvLy8MHz4cPTs2RPB\nwcGYOnUqfHx8zBw5EZH8KIRa/rQZHx+POXPmQKPRYMqUKZg/f77uYj1t2jQA0M1s1KxZM2zYsAG9\nevUyfeTU4DF3SCrmDjVWCoWCdxyJiFCH66FgZPHx8YKnp6fg7u4uLF26tNo2b775puDu7i707NlT\nOHHihFH7//bbb4WePXsKPXr0EPr16yekpKQYNXZBEISkpCTB2tpa2LZtm1FjFwRB2Ldvn+Dv7y88\n/vjjwsCBA43a/40bN4Rhw4YJfn5+wuOPPy5s2LBBdN8RERFChw4dBF9f3xrb1OW8iom/rvtg7kjv\nvzHnjinzRmzsgsDcqUldc6ehMcGvZSIiiyT1emjUq2h5ebnQrVs3ISMjQygtLRX8/PyEc+fOVWqz\nc+dOYcSIEYIgCMLRo0eF4OBgo/Z/+PBhIT8/XxAE7S9dsf2L6ftBu0GDBgnPPPOMsHXrVqPGfvv2\nbcHHx0fIzs4WBEH7ocCY/f/9738X3nvvPV3fbdq0EcrKykT1f+DAAeHEiRM1fsioy3kVGz9zh7kj\nNX6p+zBl3ojt/0E75k5Vdc2dhojFDRGRltTrod4xN4Yy9foUYvoPCQlBy5Ytdf3n5OQYrW8AWLly\nJcaNG4f27duL6teQ/uuyyJuY/h0dHVFQUAAAKCgoQNu2baFU6p1TQqd///5o3bp1je/Xdd0R5k7d\n+mfumCZ3TJk3YvsHmDs14XpHRET0KKMWN2IWMqtpfQpj9f+wdevWYeTIkUaNPTY2VrcauiFTzJp6\nkTcx/U+dOhWpqalwcnKCn58fli9fLrp/Kfs35EMec6du/TN3TJM7pswbQ2Jn7ojfvyG5Q0RE8iPu\nz2ciGXt9Cqn9A8C+ffuwfv16HDp0yGh9z5kzB0uXLtUNcHr0OOravyGLvEnpf/HixfD394dKpUJ6\nejqGDBmClJQUNG/eXNQx1EbqeTWkLXOneswd0+zDlHkjtn/mjn51yR0iIpIfoxY3xlyfQmr/AHD6\n9GlMnToVCQkJeh9pMLTv5ORkhIeHAwDy8vIQHx8PGxubKtPUSu3fkEXepPR/+PBhfPDBBwCAbt26\noUuXLrhw4QICAwNr7d/Q/RtyXqvbnrljWP/MHdPkjinzRmz/zB3x+zc0d4iISIaMMN5Hp6ysTOja\ntauQkZEhlJSU1Dqw98iRIwYNABXT/+XLl4Vu3boJR44cMXrsD3v11VcNmrVITP/nz58XnnrqKaG8\nvFwoLCwUfH19hdTUVKP1/9ZbbwmRkZGCIAjCtWvXBGdnZ+HmzZuijyEjI0PUwF5Dz6vY+Jk7zB2p\n8UvdhynzRmz/D2PuVFbX3GmIjPxrmYjIYkm9Hhr9Krpr1y6he/fuQrdu3YTFixcLgiAIq1atElat\nWqVrM3PmTKFbt25Cz549heTkZKP2P2XKFKFNmzaCv7+/4O/vLwQFBRk19gcM/ZAhtv9//vOfgo+P\nj+Dr6yssX77cqP3fuHFDePbZZ4WePXsKvr6+wpYtW0T3HR4eLjg6Ogo2NjaCi4uLsG7dOqOeVzHx\n13UfzB3p/Tfm3DFl3oiN/QHmjvFzp6FhcUNEpCX1eljrIp5ERERUP7iIJxGRltTroVFnSyMiIiIi\nIjIXFjdERERERCQLLG6IiIiIiEgWWNwQEREREZEssLghIiIiIiJZYHFDRERERESywOKGiIiIiIhk\ngcUNERERERHJAosbIiIiIiKSBRY3REREREQkCyxuiIiIiIhIFljcEBERERGRLLC4ISIiIiIiWWBx\nQ0REREREssDihoiIiIiIZIHFDRERERERyQKLGyIiIiIikgUWN0REREREJAssboiIiIiISBZY3BAR\nERERkSywuCEiIiIiIllgcUNERERERLLA4oaIiIiIiGSBxQ0REREREckCixsiIguxfPly9OjRA76+\nvli+fDkA4NatWxgyZAi6d++OoUOHIj8/X9d+8uTJ8Pf3x86dO80VMhERUb1icUNEZAHOnj2LtWvX\n4tixY0hJScEvv/yC9PR0LF26FEOGDMHFixfx1FNPYenSpbr2nTp1QnJyMjZv3mzm6ImIiOoHixsi\nIgvwxx9/IDg4GE2bNoW1tTUGDhyIbdu2IS4uDpMmTQIATJo0CT///DMAQKlUorCwECUlJeYMm4iI\nqF6xuCEisgC+vr74/fffcevWLRQVFWHXrl3IyclBbm4uHBwcAAAODg7Izc0FAHh5eaG8vBwDBw7E\nzJkzzRk6ERFRvVGaOwAiIqqdl5cX5s2bh6FDh6JZs2bw9/eHtbV1pTYKhQIKhUL3+ssvv6zvMImI\niMyKd26IiCzE5MmTcfz4cezfvx+tW7dG9+7d4eDggGvXrgEArl69ig4dOpg5SiIiIvNhcUNEZCGu\nX78OAMjKysL27dvx4osvIiwsDJs2bQIAbNq0CWPGjDFniERERGalEARBMHcQRERUuwEDBuDmzZuw\nsbHBl19+iUGDBuHWrVt44YUXkJWVBTc3N/zwww9o1aqVuUMliRQKBfhrmYhI+vWQxQ0REVEDweKG\niEhL6vWQEwoQETVgubm5OHHiBDIzM3H16lWo1Wrk5OTg+vXruH79Ou7fv4+ysjIIggClUglra2s0\nadIELi4ucHR0hKOjI5ycnODk5AR3d3cEBASgRYsW5j4sIiIik6i3OzcPz+BD8sO/NBLVXUFBAQ4e\nPIhjx47hxIkTSE5Oxr1799ClSxf06dMHjo6O6NChAxwcHODm5oYOHTqgadOmsLGxgZWVFTQaDcrL\ny1FYWIirV6/i6tWrSE9PR3p6OkpKSvDHH38gJSUFjo6OCA4ORmBgIAIDA9G3b1/Y2tqa+/AJvHND\nRPRAg38sjRds+eK5JZIuKysLO3bsQFxcHA4fPoyAgAC4urpi9OjRCAwMRJcuXUT9cUilUiE0NLTW\nduXl5Th37hySk5ORnJwMlUqF7OxsjBgxAmFhYRgxYgRat25thCMjKXg9JSLSYnFDZsNzS2SYK1eu\nYN26ddi2bRvUajV69uyJGTNmYOjQoWjevLmkPsUWN9W5evUqdu7cibi4OPz222/o3bs3XnzxRbz0\n0kt47LHHJPVJ0vB6SkSkxeKGzIbnlqh2giBApVLhq6++QkJCAl544QVEREQgJCSkymKc5lRUVIQ9\ne/Zg48aNUKlUmDhxImbOnAkfHx9zh9Yo8HpKRKTF4obMhueWqGb379/H2rVrERUVBWtra8ycORMv\nv/yyRQzqz87OxqJFi/DLL7/Ay8sLs2fPxpgxYziG0oR4PSUi0mJxQ2bDc0tUlUajwebNmxEZGQkP\nDw/MnTsXI0aMMFlhUJfH0mpTWlqKn3/+GZ988gmUSiU+++wzDB482CT7aux4PSUi0pJ6PbQyQSxE\nRI2WIAiIjY2Ft7c31q9fj++++w579uzByJEjLfaOh62tLV544QWcOnUK77zzDl5//XUMGzYMJ06c\nMHdoDUZCQgK8vLzg4eGBZcuW1dju2LFjUCqV2L59ez1GR0TUePDODdUZzy2RVmpqKqZNm4aCggJ8\n+OGHGD9+vMUWNPqUlpZi7dq1WLBgAYYPH47ly5ejXbt25g7LbDQaDTw9PbFnzx44OzsjKCgI0dHR\n8Pb2rtJuyJAhsLe3R0REBMaOHVulL15PiYi0eOeGiMhMysvLsXjxYvTv3x8vv/wyTp48iRdeeEGW\nhQ2gvZMzY8YMZGVlwdHRET169MC2bdvMHZbZJCUlwd3dHW5ubrCxsUF4eDhiY2OrtFu5ciXGjRuH\n9u3bmyFKIqLGgcUNETUKkydPhoODA3r06KH73oIFC+Dn5wd/f3889dRTyM7OBgBkZmbCzs4OAQEB\nCAgIwIwZM3Tb7NixA35+fpg6dSoA7d2afv364ddff0VycjLeeOMNs8x+plKp6n2fzZo1w2effYZt\n27bh3Xffxfjx45GXl1elnUajQUBAAEaNGgUAiIyMhIuLi+7nGx8fr2s7efJk+Pv7Y+fOnfV2HHWl\nVqvh6uqqe+3i4gK1Wl2lTWxsLKZPnw6AC1sTEZkKixsiahQiIiKQkJBQ6XvvvvsuUlJScOrUKYwZ\nMwYLFy7Uvefu7o6TJ0/i5MmT+Oqrr3Tf37JlC06ePImOHTvib3/7GwYOHIgpU6Zg37596NKlS70d\nz6NKS0tfnGfKAAAgAElEQVRRWlpqln3369cPZ8+ehbOzM3r06FGlMFm+fDl8fHx0H+gVCgXmzp2r\n+/mOGDECAHD27Fl06tQJycnJ2Lx5c70fh1RiCpU5c+Zg6dKluscs9D1qERkZqftnjqKViMgcVCpV\npeufVErjhURE1HD1798fmZmZlb738IKZ9+7dEzVupKKiAvn5+di6dStKSkpw5MgReHh4GDvcKn7/\n/XcolUqEhIQAAHbt2oW2bdsiODgYAGBlZYXU1FQEBAQAAH755Rc4ODggKCgIAJCcnAxXV1d06NDB\nJPHZ2dnhX//6F8aNG4fw8HDMmjUL8+bNg1qtxq5du/DBBx/giy++AIAaP9wrlUoUFhaipKTEJDGa\nirOzs+6uH6CdQtvFxaVSm+TkZISHhwMA8vLyEB8fDxsbG4SFhVXpry6/1ImILFVoaGilWT8f/oOj\nIVjcEFGj9sEHH+Cbb76Bvb09jh49qvt+RkYGAgIC0LJlS3zyySd48sknAQDPPfccXF1d0blzZ6Sm\npsLOzs4kcR08eBDFxcUYMmQIAKB3795o2rSp7v2RI0dWav/0009Xev3ss89Wet2uXTtoNBrd6x07\ndiAkJMToEwE8+eSTOHr0KIYOHYozZ86guLgY//znP1FQUKBro1AosHLlSmzevBmBgYH4/PPP0apV\nK3h5eaG8vBwDBw7E559/btS4TCkwMBBpaWnIzMyEk5MTYmJiEB0dXanNn3/+qfs6IiICo0aNqraw\nISKiuuFjaUTUqP3jH/9AVlYWXn31Vbz11lsAACcnJ2RnZ+PkyZP44osv8OKLL+Lu3bs4cuQI5syZ\ngw8//NDohU1WVha+++473esnn3xSV9gAgL29Paysar5k1/b4UufOneHo6Kh7HRISgiZNmuheX7p0\nSULU1XNxcUFycjLUajUOHjyI9u3bV7pTM336dGRkZODUqVNwdHTE22+/rXvvyy+/xLFjxzBgwACj\nxWNqSqUSUVFRGDZsGHx8fDBhwgR4e3tj9erVWL16tbnDIyJqVDgVNNUZzy1ZiszMTIwaNQpnzpyp\n8l5WVhZGjhyJs2fPVnlv0KBBePbZZ7Fs2TKsXbvWaH9xz8/PR6tWrQAAJSUlUCqVkicjqOsinjEx\nMXjuuedga2sruY9HzZ8/H1999RXu3buHNm3aoKioCGPHjq00nkbfOWmMeD0lItLiVNBERAZKS0vT\nfR0bG6sbr5KXl6d7hOvPP/9ESkoKPv30UyQkJBitsKmoqMCmTZt0F+4mTZrUaZa1uhQ2ADBhwgRd\nYZOdnY3169fXqT8AWLJkCe7cuYMNGzagoqICgYGB2Lx5M65evapr89NPP1WawY6IiKgueOeGpBEE\n4JtvgLQ0KD75hOeWGryJEydi//79yMvLg4ODAxYuXIhdu3bhwoULsLa2Rrdu3fD111+jQ4cO2L59\nOz766CPY2NggPz8fN2/ehEqlQq9eveoUQ05ODgoLC+Hp6WmkozIdjUZj1CmtIyMjsXTpUiQmJuKz\nzz5DSkoKFAoFunTpgtWrV8PBwcFo+7Jk/F1JRKQl9XrI4oakmTgR+P57YPZsKFas4LklWdq+fTtm\nzpyJhIQE7N+/H7Nnz65Tf8nJyejevXulWdqMpa6PpemzadMm9O/fH127dpW0fXFxMTZt2gR7e3u8\n/fbb2L9/P3x8fIwcpTzwdyURkRaLG6o/O3YAYWHAyZOAvz/PLcnS3r17MXHiROzevRsBAQG4ffs2\nWrdubXA/J0+ehP///j8xJVMWN3VVUVGBgoICtGrVClu2bMG8efNw+PBhdOrUydyhNTi8nhIRaXHM\nDdWft94C3nkH8Pc3dyREJpGeno7w8HCsWbNGNw5HSmFTUVFRaf0TU6qvwuaPP/7ATz/9ZNA2VlZW\nuokTXnrpJcyZMwdjxoxBUVGRKUIkIqJGjHduyDDbtgHjxgGFhYC9PQCeW5KXgoIChISEYNasWZg+\nfXqV92/duoUtW7bgzTffrLGPiooKvdM2W7r79+9XWnOnOoIg4OOPP8aCBQuq3LUSBAEjRoxAixYt\nEBMTY/K7WpaE11MiIi0+lkamV1EBODgAU6YAS5fqvs1zS3JRUVGB0aNHw9nZGatWrdLbrqbiJS8v\nD99//z1mzZplqjCrZY7H0oqLi5Gbmws3N7dq39f3c7p//z4GDhyI0aNH4/333zdhlJaF11MiIi0+\nlkamFxkJ3LoFLFxo7kiITOKjjz7Cn3/+iRUrVuht9+ADe2lpaZULb7t27Uxa2AiCUGmfW7duxa1b\nt3Sv4+LiUFBQYLL9P8za2hrJycmVvvfwz0Tf3aumTZvip59+wldffYW4uDiTxklERI0HixsS59Il\n4OOPtTOkPbSqOZFcHDhwAOvXr4dKpRK9kOWlS5fw888/A9BOHGAKhYWFKCws1L1etWoVrl+/rnv9\n5JNPokWLFrq7Nn5+fmjy0P+jX3zxBfLz83WvKyoqjBabra0txo4dW+l7q1atqhSvPk5OToiJiUFE\nRARyc3ONFhcRETVefCyNxPHz0z6S9uuvVd7iuSVLV1hYCD8/P3z++ecYPXq0wdvfunULZ86cwcCB\nA40e2/bt29G3b184OTnVuS9BEPCPf/wD77//vtHHBN28eRMHDx6U9PN77733cPHiRWzbtq3Rj7/h\n9ZSISItjbsh0Fi7UPpJ24wbQrl2Vt3luydK9+eabSEtLQ0JCguQ+rly5AkdHxzp/OD9w4ADKy8sx\nePBgg7aTMuYmJycHe/bswauvvmrQdo8qLCxEWVkZ7ty5g86dOxu8/f3799G7d28sWLAA4eHhdYrF\n0vF6SkSkxeKGTCMxEejbF9i+HXjuuWqb8NySJTtw4ADCw8Nx9uxZtGnTxqBtf/31VwwZMgQKhQLH\njx+HlZUVevXqZXAMUtfQeZjUCQVKS0tFP4ZXk9jYWPTt2xcODg6S+0hKSsLIkSORmppap34sHa+n\nRERaLG7I+PLygPbtgYgIYP36Gpvx3JKlun//Pnx9fSU9jiYIAg4fPownnniiTjGUlJRg3bp1mDFj\nRp36MYbo6Gj4+/vD29u7Tv3s27cPjo6O8PLyMmi7N954A7du3cIPP/xQp/1bMl5PiYi0WNyQcQkC\n4OsLlJUBf/wB6Hk+n+eWLNUXX3yB+Ph4/Pe//zVqv0eOHEHfvn31PqJWVlYGGxsbo+63PhUWFuLS\npUvw8/Or8l55eTlu376N9u3bG9RncXExPD09ERMTg5CQEADA5MmTsXPnTnTo0AFnzpypdrvZs2cj\nPj4e9vb22Lhxo27hVUvE6ykRkRangibj+stfgHPngMOH9RY2RJaqoKAAS5cuxaeffmrQdnfv3sUf\nf/yht42trS3y8vJqfP/IkSPYv3+/QfutjUqlMmp/aWlp2L59u973O3bsWO17SqXS4MIGAOzs7BAZ\nGYl58+bpfqFFREToHQu1a9cuXLp0CWlpaVizZk21C68SEVHjwU+tVNUXXwDffqsdb1PNBAJEcvDZ\nZ59hxIgRBv+V/9y5c2jbtq3eNr1799b74T4kJARPP/20Qfutbx4eHhg+fHiN7/v7+9c6NqaiogLx\n8fEG7fcvf/kLMjIydAVN//799Y5HiouLw6RJkwAAwcHByM/P57TSRESNGIsbqmzXLuDtt4HoaKBP\nH3NHQ2QSubm5+Pe//41FixYZvG1wcLBBdyW+//57CIKACxcumGwtHACSJhOojb29PQDtuKCCggIU\nFhYatOCmlZVVrYXgo5RKJVasWIH58+eLWpNHrVbD1dVV99rFxQU5OTkG7ZOIiOSDxQ39v+Rk4Jln\ngA8/BBr5dKwkb5988gkGDhxo0LTFxcXFkp797d27NyoqKqBQKNCjRw+Dt28IioqKEBsbi5KSEgQH\nBxu0bZ8a/kiSkJAALy8veHh4YNmyZZXeGzNmDGxsbBAQEAB/f38MHToUt2/frnEfj56Xxr5WDhFR\nY8bihrTOnwcCA4GJE4GPPzZ3NEQmU1BQgG+//RYrV640aLsNGzbg7t27Bu/Pw8MD1tbW6N69O5RK\npcHbi2XsMTcPa926NV555RW0adNG8jTND49B0mg0mDVrFhISEnDu3DlER0fj/PnzuvcVCgXc3d1x\n/fp1nDp1Ct9//z2uXbuG8vLyKv06OzsjOztb9zonJwfOzs6SYiQiIsvH4oa0hY2PDzBypHasDZGM\nffPNNxgyZIjBH4BnzJiBFi1aGLTNlStXsHXrVgDaGcT+9a9/GbR9TYqLi/Hvf/9b97qgoKDS9Mn5\n+flYu3atUfZVUlJSqRD8888/UVJSYnA/0dHRujssSUlJcHd3h5ubG2xsbBAeHo7Y2NhK7QcMGIC7\nd+8iOTkZ9+7dg7W1dbXFYVhYGDZv3gwAOHr0KFq1atWo18khImrsOBV0Y6dSAYMGAcOHa8fbSHic\ng+eWLIUgCPDx8cHXX39tkjEqj3rwodzOzg6Adqa15s2bS+pr1apVmDJlCmxsbFBRUYHi4mI0a9as\nxvYP7ysjIwNnz57FqFGjDN6vIAi4d++erq/Lly9DrVajX79+ko4DALZu3Yrdu3fjP//5DwDg22+/\nRWJiYqUiqqKiAu7u7sjKykJFRQWsra3RsWNHLFy4EGVlZQCAadOmAYDuLlCzZs2wYcMGSQupNhS8\nnhIRaUm9HpruGQlq2DIytI+fbdgAzJgBREVJKmyILMmBAwdw//59DBw4UPQ2hw8fRrdu3STdDXjs\nsccqvX64sBEEwaCxIePGjdOti2NlZaW3sHl0X126dDG4qHoQn0KhqLRt586dDRqrVB0xx7148WI8\n/fTT+PHHH7F3716MGzcOKSkp1R5HVFRUneIhIiL54GNpjUVWlnbNmhkzAHd3oGtXIDUViI8H/v1v\nFjbUKHz99deYO3euQUWFvb09OnToYNB+EhISUFxcXOP7169fx9dff623j19++QXHjh3TvW5Xy7Ts\ntY25eXj7bdu24fTp03rbL1q0qNbZyqobg6RvogBBEBATE4Pc3FzExMTA19cXoaGhyM7OhouLS6W2\nhw8fxqRJk9CrVy+oVCp06dIFFy5c0BsPERERH0uTo6Ii4MIF4OBBYO9eIC1NuyBn9+5Ap07A2LHa\n2dBatTLK7nhuyRIUFhbC0dERly9f1rtuijGcOnUK/v7+etsYeuemNiqVyqiP2tUWn0ajwfLlyzF3\n7txK3/P09MSePXvg7OyMoKAgREdHw9vbW9fm6NGjmDx5MoqKiqBSqWBra4uRI0dWaTd37ly0bNkS\nwcHBmD9/Pm7cuIHTp0+jTZs2RjvGhojXUyIiLT6W1phlZAAnTwJbtwJ//KH9GgCGDAE8PYGZM7VF\njaeneeMkMqM9e/bA29vboMJGagFSW2ED/P+jWffv30eTJk2gUCiwZs0avPzyy7r1ZQwhtbD5448/\nkJmZieHDh1eKpbbjtra2rlTYAJUnCgCgmyjg4aLlxIkTGDduHEJCQjBs2DBoNBpMmTIF3t7eWL16\nNQDtWJr3338fERER2LZtG86dO4fly5fLvrAhIqK6Y3Fjie7fB5KSgLg47exmublAjx7af++8Azz5\npLaYISKd2NhYjBw5UnT7mzdvIiYmBjNmzBC9TV5eHtq2bWtQQZSWlobLly/j2Wefxfjx4yUVNnXh\n5eWle+xu/fr1eOmll9CyZUtJfVW3oGZiYmKlNmlpaSgtLcWyZcvQvHlz/PWvf8Urr7wC4P8nCAC0\nj9Ht2LEDgLZIsrW1lRQTERE1LhxzY0lOngReeQVo1w4YOBC4eBH45BMgOxs4fRrYsgV48UUWNkSP\n0Gg02Llzp+5DtBht27bF66+/btB+vv/+e0NDg6+vL5599lkAqNPjcnVZ5+bBHZHp06dXW9jUNo7m\nwVTM6enpWLduHbZv317jvsrKyqBSqfDRRx9h9+7d+Pjjj5GWlqY3vqeeegrbtm0z9LCIiKgRYnHT\n0JWVaWc08/YGevXS3qXZvBkoLdXeuXntNeCRgbhEVFlSUhI6dOiArl27GrSdoYtuzpo1y+DH2Fat\nWoXr168DADIzM+t9vEVhYSFu3LgBQDub3KNFkpgFN5988kloNBr88MMPaNeune4YqpsowNXVFePG\njcOgQYPQtm1bDBgwACkpKXpjHDduHA4fPozCwkIjHDEREckZi5uGbNcuwMkJmDwZGDoUuHED+PVX\n4Pnngf9NCUtEtdu1axcCAwNFty8oKNA725kxTZs2TfdY2I0bN3D27FlJ/Ugdc6NSqXSzog0cOBAD\nBgyo9L6YBTe7du2KlStX4tVXX8X9+/dx/fp1lJaWIiYmBmFhYZXajh49GocOHUJFRQWKioqQmJgI\nHx8fvTG2bt0avXv3xv79+yUdIxERNR4sbhqi1FQgIAB45hlg4kTg9m1g+XLt42hEZLBjx45h+PDh\notvv3r272mmOa5Keno6LFy9KCQ1WVv9/GQ4KCkKPHj0k9SPVM888U2kNn4fjAaofR6NWq6u0+fnn\nnzF9+nQEBwfjk08+gY+PDyZMmKCbKODBZAFeXl4YPnw4evTogd69e2Pq1Km1FjcAEBgYiKSkpLoc\nKhERNQIsbhoSjQaYOxfw9QXatgWuXgVWrDDalM1EjZEgCDhx4gSeeOIJ0duMHz/eoLVtiouL4eTk\nZFBca9eu1fv+gQMHRD2i9uCui0qlwv3791FaWlrrNoWFhZXW0HlUaWmpbhyNmMfs5syZg4iICN0U\n0CtWrMClS5cwf/58ANq7Uw9PFvDOO+/g+PHj+Nvf/obZs2fX2j8A9OjRo8rkBERERI/ibGkNRXIy\nMGqUtqDZvh147jlzR0QkCzk5ObCysoKzs7PJ9uHr62tQe0EQar2T9Nhjj+HOnTtopeePG2fOnMHF\nixcxduxYANoxO5cuXdJNUFDTVNYP2r3yyivQaDR47bXXMG/ePN37tra2uH37Nvz8/FBYWIibN2/i\nzTffRM+ePasdR5OcnIzjx48D0M4YFx8fDxsbmyqPpD3M3t4ekydP1vszeFj//v11xRIREVFNuIin\nuZWUaKdvjorSFjdbtgDNm5s7KoPw3FJD9tNPP2Hx4sV671Q8TK1Wo3nz5mjRooWJIzNceXk5li9f\njrfffltU+9jYWLi6uqJXr16Vvi9msc0jR47Ax8cHzZo1Q6dOneDg4IDExET06dOnStuHRUREYNSo\nUXj++eelH2g1BEFAu3btkJqaio4dOxq174aE11MiIi2p10M+lmZOv/8OODtrC5udO7Wzn1lYYUPU\n0CUnJ2Pw4MGi26empqKsrEx0+w0bNkCj0YhuX1BQoHuUTKzNmzdDEAQolUq88cYborcbPXq0rrAp\nLCzE1q1bAYibJCAkJARNmzaFRqPB8uXLcfbs2RrH0TxQUlJi0EQMd+7c0c3UVhuFQoHHH38cycnJ\novsnIqLGh8WNORQVadejGTAAGDkSuHtX+18iMrqLFy8iICBAdPuhQ4eibdu2ots/9dRTsLa2Ft1+\n48aNBhVDAODn56f761WzZs2qbVPbOjfHjx+Hu7s7AHGTBADax9d+/fVXXL58GREREXrH0TzwzDPP\niL5rc+/ePZw+fVpUWwDo2LEjLly4ILo9ERE1PhxzU98OHtRO62xlBRw5AvTta+6IiGTtypUrBg/2\nN0QnAxfNFTuA/oGKigr89ttv8PPzM2i7R/Xp0wcZGRkAxE0SAACenp64cuUK1q9fj0OHDtXavkmT\nJgYtlOrs7GzQWKjevXvj6tWrotsTEVHjwzs39aWwUHu3pn9/4IUXtGvWsLAhMrnLly8bVNzUtqBk\nfbOyssJbb70FQDuL2Zdfflltu+rWuSkrK8Pnn38OALCzs9NNuezs7Izs7Gxdu+omCQCA06dPY+rU\nqYiLi0Pr1q3reih15uTkhCtXrpg7DCIiasBY3NSHnTuBxx4DEhK0d2s2bgTs7MwdFZHsCYKAvLw8\nODo6impfXl6Oy5cvi+4/Li4OmZmZotvX9ZEqW1vbah8Fq4mNjQ2mT59e6XvFxcVwd3dHWloaMjMz\na1xsMysrC88//zzWrVsHGwMWDb569Sry8/NFtz9//rzoto6OjtU+PkdERPQAixtTUquBp54Cnn0W\nmDVLO80z79YQ1Zv8/HzY2trWOE7lUUqlUu/0xY8aMGBAtXc8anLixAnRbQHtZAWPsre3B6At3B6e\nRebhMTcPf/9B+wcKCwuxb98+REVFYdiwYTVOErBo0SLcvn0bs2fPRmhoKPr06SMq5mvXruHatWui\nj/HUqVOi2zo5OSEnJ0d0eyIiaoSEelKPuzK/wkJBeO89QQAEwdtbEFJTzR2RSTWqc0v1KiIiQujQ\noYPg6+ur+15iYqIQFBQk+Pv7C4GBgUJSUpLuvcWLFwvu7u6Cp6ensHv3buHcuXNC9+7dhbi4OKFn\nz57Ca6+9Zo7DkCwjI6PG93JycoQ1a9boXu/bt0/39aJFi4Ty8nITRmYet27dElq0aCEIgiDEx8cL\nnp6egru7u7B06VJBEAQhPT1dCAoKEgYPHizcvn3bnKFKxuspEZGW1Osh17kxtlWrgHff1c6ItmWL\ndnyNyMG7lqrRnFuqd7///jsee+wx/OUvf8GZM2cAaMeWzJ8/H8OGDUN8fDw+/fRT7Nu3D+fOncOL\nL76IY8eOQa1W4+mnn0Z0dDTCw8MRFBSE7777DpGRkZgwYQIef/zxaveXnZ0NOzs7tGvXrj4PUzKh\nhkU6a/q+pSstLUWzZs1w//79atfpWb9+PWbNmoX09HScP38eM2fONHfIBuP1lIhIi+vcmFtcHNCl\nCzB9OvDee8C9e8CECbIvbIhMqX///lUGsjs6OuLOnTsAtI+dPZhtKzY2FhMnToSNjQ3c3Nzg7u6O\nlJQUtGnTBhUVFSgpKUFRURFsbW1r3N+NGzdQWFgoOr7ly5eLbltaWoqLFy+Kbi/GgwKmsLAQRUVF\nuimmaytsrl27hlu3bonez7lz5wyKy5D2ly9fFv0zVyqVKC8vR2JiYrXr9CiVSty7dw/37t0zaJwQ\nERHJB4ubusrIAIYMAUaP1k7xfPcu8P77QNOm5o6MSJaWLl2Kt99+G506dcLf/vY3LFmyBIB2yueH\nx7+4uLjg2rVrsLW1xeuvv47+/fvD2toaHh4eNfbdq1cvdO7cWXQshkx7XFRUZNDkAxs3bhS9IOaZ\nM2cwcOBA0QP5U1NTcfbsWdGxPLhrZor2qampuH37tqi2VlZWUCgUyM7OrnadnpkzZ2LmzJlYv349\nXn75ZYNiJiIieeA6N1KVlQGRkcDixUC/fkBqKvC/aVaJyHSmTJmCFStW4LnnnsOPP/6IyZMn47//\n/W+1ba2trVFRUQGlUonPPvtMN13yg8H39f166NChotu3bNlSd5eptvZnz56FjY0NDh48iNGjR9e6\nTbt27XD69GmoVCpRx+Dn52fQMU+YMEF0+5H/W8BYTHvhf5Mo1LRoqouLS62LmRIRkbxxzI0UKhUw\nahRQXg6sXw9MnGjuiMxKVueWGpzMzEyMGjVKdzegRYsWKCgoAKAdW9KqVSvcuXMHS5cuBQC89957\nAIDhw4djwoQJ+Mc//oFLly6J2ldOTg5sbW3RoUMHExyJaT1cqBhbdHQ0Joq8zpWXl+PatWsGzSIn\nVnl5OZo2bYqDBw8iMjISCQkJAIAlS5bAysoK8+bNM/o+6xuvp0REWhxzUx+KioBJk4BBg4Dnnwdu\n3mz0hQ1RfXN3d8f+/fsBAL/99hu6d+8OAAgLC8P333+P0tJSZGRkIC0tDQEBAbAzYE2p/Px83Xge\nMf71r38ZFLuhj3fpU1hYiNjYWN3rhwub6Ohoo35AFlvYAMCdO3eQnJwsur1arUZFRYWotiUlJbCx\nsUFgYGCt6/QQEVHjxOJGrJ07gZYttRMH/PYbsGkT8Mj6EURkXBMnTkS/fv1w4cIFuLq6YsOGDViz\nZg3effdd+Pv748MPP8SaNWsAAD4+PnjhhRfg4+ODESNG4KuvvkKHDh1w48YN0fvz9fXVOybnUZMm\nTTLoeAxdxHPZsmU1vldWVoa+NaybFRQUVGPBUFZWhqSkJIPiMETbtm11j8aJER8fL3pmt+vXr6N9\n+/ZQKpXVrtNDRETEx9Jqc+cOEBEB/PQT8MYbwIoVAGfhqcRizy3JXnl5Oezs7FBcXAyl0vKGGBYV\nFVVZhLMmYh9Ly83NxdWrV+Hv7y+q3xs3bqCsrAxOTk6i2pvSoUOHMHv2bIPuDFkaXk+JiLT4WJqx\nCYJ2zZpWrYBTp4DERODrr1nYEFkQpVKJ1q1bIzc3V/Q2DemD86OFTWlpKVasWCF6+7KysiqPzjk4\nOIgubADtmKfy8nLR7dPS0kS3NdTVq1fh5uZmsv6JiMjysbipTlYWEBSkXbNm8WLgzz+BPn3MHRUR\nSeDq6oorV66Ibm9I26KiIqxcuVJ0e0EQcPjwYdHtAaCiogKJiYkAABsbG0yePLnadtXdtbGxscFr\nr72me/1gHRxDBAUFoVOnTqLbP4hVrIyMDNFtr1y5AkdHR4P6r08JCQnw8vKCh4dHtY8UbtmyBX5+\nfujZsyeeeOIJnD592gxREhHJG4ubhwkC8PnnQOfOgLU1cPkyMH++uaMiojpwcnLC1atXRbcfNWqU\n6Lb29vY1FhvVUSgUuHfvnuj2gHZtl7y8PN32jz32mEHbP2h/+PBh7N2716BtpTBkfZmSkhKDir0/\n//yzQTweVx2NRoNZs2YhISEB586dQ3R0NM6fP1+pTdeuXXHgwAGcPn0aCxYswOuvv26maImI5IvF\nzQNnzwJeXsA77wArV2ofQzPgr5VE1DB17twZ6enpJuu/WbNmBrV/sNaNIZKTk2udUay29V26du1q\n8M/hv//9L0pLSw3axhBNmjTBSy+9JLr9yZMnDbqLVJ+SkpLg7u4ONzc32NjYIDw8vNJsdgAQEhKC\nli1bAgCCg4ORk5NjjlCJiGTN8kbYGpsgAAsXav89/TRw+DDQtq25oyIiI+nVqxc2btyIt956S1R7\ntTII60gAAB48SURBVFqNkpISdO3aVfQ+ysrKYGPC8XgLFiyAQqFAeXk5/vnPf2K+yDvKZ86cQXl5\nOQICAtCxY0e88cYbBu334YVExUhOTkavXr1Ez35mKLVajV69epmk77pSq9VwdXXVvXZxcdH7iN66\ndet0C5g+KjIyUvd1aGioydYvIiJqSFQqlVEWYm7cxc3+/cBLLwFqNbBxo3YNGyKSld69e+Ozzz4T\n3d7e3h7Xrl0T3b6oqAhff/013n77bdHb7N69G126dNGt0VOd+/fvw9bWFlZWVrpiQalU4p133tG1\nycvLQ1FRETp16oTQ0FCUlJTg7t27aNeuHQDtI2kP3+l40E9xcTGaNm1aaxHSx8CxhleuXEHv3r1F\ntz979ix8fX1Ftc3Pz0dubi48PT0Niqm+GFLQ7du3D+vXr8ehQ4eqff/h4oaIqLF49I85CxculNRP\n43wsrbAQePFFIDRU++/uXRY2RDLl4+ODy5cv4+7du6Lat27d2qAP6Pb29pg7d65BMQ0cOBAdO3bU\n22bTpk0oKCio8v2H7xBpNBqo1Wrd68zMTJw9e1b3ukuXLrC2tq7SR1paGuLj42vcd1lZmaTpNw0Z\nrwQA586dE932+PHj6NGjR7XH0xA4OzsjOztb9zo7OxsuLi5V2p0+fRpTp05FXFwcWrduXZ8hEhE1\nCo1vnZsNG4DJkwFHRyAmBujf39wRWbwGc26JahAcHIxPP/0UAwcONHcoJiN2nRsxVq1ahbFjx6J9\n+/ZG6c8YPvzwQ9y5c8eg2enqU3l5OTw9PbF37144OTmhT58+iI6OrrS4aFZWFgYPHoxvv/22xgVY\neT0lItLiOje1+fNP4IkntIXNRx9pH0VjYUPUKAQEBODnn38W3f7q1as4evSoQfu4cOGCoWEhOzsb\nWVlZuteFhYUGrcljDBkZGVV+ebzxxhsGFTYajQY//vijsUOrJC0tDUFBQSbdR10olUpERUVh2LBh\n8PHxwYQJE+Dt7Y3Vq1dj9erVAIBFixbh9u3bmD59OgICAgx+7I+IiGon/zs3gqBdq+bDD7XFzTff\nAF261H8cMsa/NFJDt3XrVqxduxYJCQmi2ms0GqSmpqJnz56i9/Hjjz9i9OjRBg3ALykpwdGjR3V3\nlOLj49GrVy84ODiI7qOujh8/Djs7Ozz++OOS+yguLsa1a9fQxYBr62+//YbBgweLalteXo6OHTvi\n5MmTlQbtyxGvp0REWlKvh/IubpKTgfHjgYwM4D//AR5azI6Mh7+MqaG7e/cunJ2doVar0bx5c3OH\n0yAVFhZi1apVBk2MIFVpaSmOHz+Ofv36iWq/b98+vPXWWzh16pSJIzM/Xk+JiLT4WNrDBAH44AMg\nMBB4/HEgP5+FDVEj1rx5c4SEhGDHjh3mDqWKwsJCJCUlYf/+/SguLpbcT12nz2zWrBn8/f0N/kVS\nVFRk8L5sbW1FFzaA9q7YmDFjDN4PERE1PvIrblJTtY+dLV4MREcDO3YA/1s0jYgar7CwMHzzzTcG\nbfP5558bvJ+oqCiDCoTLly+jc+fOcHd3x/Xr1w3eX13dvHlT93Xr1q2rnaGtJsL/tXfnYVFddx/A\nvyAjomDUqMjmEiGyKesL2gQFAq51J+5taihujdU82kexS2oeUrc8sVKXSqM1DWjUVCVaUMRoNAQE\nFFBBESMoIIoUEGFYh/v+Ma/3lSg4w2zMzPfzPP5xl3PP7x6SM/Obe+45gqCVF/zPnTun9ExsRERk\nnAxnWJogANu3A2vWAMHBwLFjTGq0hMMoSB/cv38f3t7eePjwIczMFFviq6amBr1791aqnvLycgwY\nMEClhSw1vSjoM/X19YiNjUVERITG63rm6NGjCAwMVHjCgvz8fAQHB6OkpERji4N2JexPiYjkjHtY\nWnW1fL2aNWuAPXuA5GQmNkTUxuDBgzFs2DAkJycrXEbZxAYABg4c+Mov4XV1dR3OLnbt2rUO16FR\nVUtLCwDAwsKi3cTmiy++0MiX7LFjxyo1E9tnn32GGTNmGEViQ0REqtP/5CYjA+jbFygokP9btgzg\nhyARvcTSpUuxZcsWpco0NDSgoqJC6bouXbokJhE/JQhCh2vu+Pj4YNKkSeK2IsPVFH3nJikpCZmZ\nma88b+zYse0ee/z4MeLi4hSq76eUmQmuubkZ8fHxWL58eafqIiIi46Pfyc0//gH4+QHz5wP37wOO\njrqOiIi6sPnz5yM7Oxv37t1TuExrayvOnDmjdF0DBgxoNymxtLTEwIEDFb5WQkIC6urqlI4BACor\nK3H8+HFxe/z48e0uIPm8YcOGtfu0pHfv3kq/4N/S0qJ0khgfH48RI0bA3d1dqXJERGS89DO5EQRg\n1SpgyRJg507g4EFAwTH0RGS8evXqhffeew8xMTEKl+nZsycWLlyodF3Ozs6wtbUVt1taWvDpp58q\nfR0A+NWvfoVevXoBkL8ns3nzZvHY06dPxfdYAKC6uhp//etfxePm5uYdPoV5lcbGRmzfvr3NPnNz\nczEeRaWlpaGsrEypMrt378aKFSuUKkNERMZN/yYUEAQgLEw+YcDZs0BIiOrXJJXwBVjSJ/n5+Rg3\nbhzu3bsHc3Nzjdcnk8mQnp6OMWPGoL6+HhYWFmq9fktLCx4+fAh7e3sAEP9fVOc7Ks/ijomJweLF\ni7Uy2cGNGzcQFBSE0tJSpRZG1XfsT4mI5IxjQoHGRvkwtGPHgMxMJjZEpLQRI0bA1dVV6WmeKyoq\ncOjQIaXrMzU1RW1tLQCoPbEBADMzM9jb24vv3JiYmKj95ftncU+fPl3hmeZUFR0djWXLlhlVYkNE\nRKrTn+SmsREYORK4fh0oKQF8fHQdERHpqaioKPz9739HQ0ODwmX69++P0NDQTtUVFBSkdLmu4vlf\nzZqbm7Fv3z6lyp85cwa5ublKlbl9+zaOHTuGDz/8UKlyRERE+jEsrakJcHMDysqAwkJAiWlESfM4\njIL00fTp0xEYGKjxL9CCIIhPUpqamrB3716sXLlSo3Wq06effoqlS5fCysoKQNv7UUR1dTX69Omj\nVJ1TpkzBW2+9hQ0bNihVzhCwPyUikutsf9j1k5vWVvlTmtxceXLz+uvqD45Uwg9j0kc3btxAcHAw\n7ty5o/R6NsnJyQjpYFisVCqFubk5unXr9sKxziwM2hXV1taiV69eah8Cl5mZiSlTpuDu3btKT1pg\nCNifEhHJGe47N/PnA9nZQFERExsiUht3d3dMmDABH330kdJlzc3NO+xwDx06hCdPnrz02POJTWZm\nptq+yCq6zs2ryGQyhRbwvHPnTocLoh4/frxT9xYZGYmNGzcaZWJDRESq69rzJ69bBxw5Aly5Ajw3\npSoRkTpERUXBx8cHq1atwtChQxUuFxAQ0OHx8PBwha7z5MkTSKXSLvVF3tTUFOPGjXvlExlPT88O\nj3e0Tk57Tp48iXv37incfkRERD/VdYelnTwJTJvG6Z71AIdRkD7bsmULkpKSkJycrPSX8WdDp6yt\nrVFXV4fKyko4ODh0Ko7CwkJ0794ddnZ2nSqvivz8fJSXl78yaWvP7du34eTkpNIQtaqqKjg5OeHr\nr78W1+wxRuxPiYjkDGtYWmWlPLGJjGRiQ0QatWbNGjx58gRRUVFKl7W2tsbt27cBAKmpqSpNW9yv\nXz+UlJR0urwqevfujdGjR3e6fE1NDQoKCpCfn4+8vLxOXWP16tWYN2+eUSc2RESkuq755GbcOPnk\nAfn5gJpfViX14y+NpO/y8vIwbtw4ZGRkKDU8TZPi4uLg6+uLESNGKHT+hQsXFE4Mnq3Zo+5Z227c\nuAFnZ2el18I5deoUVq1ahZycHFhaWqo1Jn3D/pSISM5wntzExgIXL8qHozGxISItcHV1xdq1axEe\nHo7W1laFy9XV1SElJQUAcO7cOVRUVKgtpoULF7ZJbHbs2IHKykqFywuCIH4oCIKAjz/+WLy3/v37\na2Q6and3d3z77bdKfRhVVlYiPDwc+/btM/rEhoiIVNe1kpumJuAXvwCiooAhQ3QdDREZkTVr1qC+\nvh4ffPCBwmXKysrg6OgIAPDz84NUKtVUeFi1ahX69u0rbm/duhV1dXXidnp6epv6t27disbGRgDy\nX7/+9Kc/wdRU/V1+ampqm5naBg4c2CaujshkMixatIjD0YiISG261rC0VauAQ4eAhw8BDXwIk2Zw\nGAUZirKyMvj6+mLnzp2YOXNmp6+j7EKX+qyhoQE9evToVNnIyEikpaUhKSkJEolEzZHpJ/anRERy\n+j8sraQEiI4GvvySiQ0R6YSNjQ1OnDiBJUuW4Pr16y89p66uDgcPHuzwOmfPnhWHq2mLuta5UURt\nbS3Ky8sBoN3ERhAEfP755+1+MO3ZswdfffUVjh49ysSGiIjUpus8uZk+Haiqkr9vQ3qFvzSSoTl4\n8CDWrVuHq1evYsCAAW2ONTQ04OnTpy/s1zVlJhRQVWJiIry9vWFtbd3heSUlJbCzs3vhKVZmZiYm\nTJiACxcuYOTIkZoMVe+wPyUikutsf9g1kpvMTOB//gfIzQVcXbURDqkRP4zJEK1btw6XLl1CcnIy\nevbs2enrlJSUIDU1Fe+++64ao9M+mUyGbt26qXydoqIiBAQEIDo6WqWhf4aK/SkRkZx+D0tbtgyY\nO5eJDRF1GZs2bYKjoyNmzpyJuro6bNmypVPXsbe3R2hoqJqj0y5BELBlyxa0tLR0qnx9fT22b9+O\n+/fvY+zYsVi3bh0TGyIi0gjdP7k5exYYPx6oqABef10boZCa8ZdGMlQtLS1YuHAhiouLkZiYiNde\ne03la+7fvx+zZs1Cnz591BDh/9PEsLSmpiaVFiZ93t27dzF+/HgsW7YMa9euVcs1DRH7UyIiOf19\ncrN8ufwfExsi0qDi4mIEBQXBzc0N7u7uiI6OBgD87ne/g4uLCzw8PDBr1iw8efIEgHz4lJWVFfLz\n85GXlwd3d3dxquWTJ0/Cw8MDERERSscxZ84c9OrVS303piFpaWm4dOmS0uUaGhrg7+8PT09PuLq6\nIjIyEvfu3cPPfvYzPH36VHyX6ZmioiJYWFjAy8sLXl5eWLFihXhMlXYmIiLjpNvk5uJF4McfgW3b\ndBoGERk+iUSC7du3Izc3F2lpadi1axdu3ryJ8ePHIzc3Fzk5OXjzzTexadMmsYyjoyOys7NRUVGB\nwMBATJo0Cf/9738RFxeHrKws2NjYIDc3V6k4LC0txdnBysvLsWvXLrXcn6pPbZqamnDq1Clxe/To\n0XjnnXeUvk6PHj1w/vx5ZGdn49q1azh16hS8vb0RHh6OS5cuYezYsS+UcXR0RFZWFrKysrB7925x\nvyrtTERExkm3yc369cDixYAe/IpJRPpt0KBB8PT0BCBPMFxcXPDgwQOEhoaKi1v6+/ujpKTkhbJm\nZmY4cOAA/Pz84ObmhqqqKjQ2NkIqlao0bGvgwIH4zW9+I25fvXoVeXl5nb6esqRSKWQyGQD54/9h\nw4ap5brPJmA4fvw4bt26hbVr1+KTTz7Bm2++qdR1Wltb1dLORERkPHSX3Fy8CKSmAlFROguBiIxT\nUVERsrKy4O/v32b//v37MXnyZHG7sLAQXl5eCAwMRGpqKrZt24atW7ciPT0dI0eORLdu3eDk5KS2\nuFxcXGBlZSVuX7lyBQ8ePFCorCLr3DQ0NKCxsVHcPnDgAOrr6wHIn2y5ubkpF3A7ZDIZbGxsMHfu\nXMybNw+RkZEdnv98O3///ffi/iVLliAgIEDt7UxERIZLN8mNTAbMnAksWQLY2uokBCIyTrW1tQgL\nC8OOHTtgaWkp7v/kk0/QvXt3LFiwAABga2uL4uJiZGVl4bPPPsOCBQvw9OlT/PKXv8SZM2fQ0NCg\n9pe/LSws4ODgIG4PGjSozfEjR460GZ5VVFSE2tpacbuyslJMVgDgzJkzuHv3rrj973//G1VVVeL2\nihUr2rSBOkilUoSFhcHGxgZ5eXkoKCjoMPFqr50BICQkBJmZmZ2eqY6IiIyPbpKbVauA6mrg/17o\nJSLShubmZsyePRuLFi3CjBkzxP0HDhxAQkIC4uLixH3du3dH3759AQDe3t4YPnw4CgoKAAB+fn5I\nT09HcnIyJk6ciOLiYo3Ea2dnB9vnfgCaM2dOm6crZWVlqK6uBiB/5yYjIwPl5eXicR8fHwwePFjc\nXrhw4QsJkzpdvnwZvr6+sLKyQkpKCpydnTFlyhRkZma2W6ajdiYiIlKW9pOblBRg1y4gORkwN9d6\n9URknARBQHh4OFxdXbF69Wpx/+nTp7Ft2zbEx8ejR48e4v6KigrxfZS7d++ioKAAb7zxhnjc1tYW\nqampCAgIgLe3Nz7//HOtT+E7ZswY2Nvbi9sTJkzAkCFDxO3+/fvDzMxM43E0NDTg17/+NaZOnYo1\na9YgOjoaFhYWqK+vx9mzZ+Hl5dXm/Ofb6VXtTEREpAztJjdSKfD220BEBBAUpNWqici4paSkIDY2\nFufPnxenHU5MTMTKlStRW1uL0NDQNlMRf/fdd/Dw8ICXlxfeffdd7N2794W1aSQSCf7whz/g3Llz\niI6ORmBgoMae4ryKIu/caMLly5fh7e2N6upq3LhxA35+fggODoanpyf8/f0xdepUvPPOOzh+/Dgc\nHByQlpaGKVOmYNKkSQAUa2ciIiJFaXcRz+XLgRMngOJioFs3bVRLWsBF54jkQ942b96MHTt24MMP\nP8Tq1au1up6NJhbx7MiDBw8QERGBzMxMREdHY86cOTAxMdFa/YaK/SkRkZx+LOK5Zw8QG8vEhogM\njkQiwR//+Eekpqbi2rVrGDJkCPbs2YPm5mat1K+txKaqqgrr16/HyJEj4ebmhps3b2Lu3LlMbIiI\nqEvQbnIzZAgQHKzVKomItMnJyQmHDx9GUlISTpw4ARcXF8TGxqKlpUXXoamktrYW69evh6OjIyor\nK5GTk4OtW7eiX79+ug6NiIhIpN3k5v33tVodEZGueHt748yZM4iJicHOnTsxePBgREVF4eHDhxqp\nT1Pv3OTl5WHlypUYPHgwfvzxR6SkpCAmJqbNRAZERERdhXaTm8WLtVodEZGuBQcHIy0tDQkJCSgu\nLoaLiwsCAgLw7bffdtl3K5qamnDkyBEEBgZi3Lhx6Nu3L3JycnD06FE4OzvrOjwiIqJ2aXdCgS76\nQU6q4d+WSHFPnjzBF198gb1796KmpkacOWzixIkw1+H0+FVVVUhMTERMTAyys7Ph6emJFStWYMaM\nGejevbvO4jI27E+JiOQ62x8yuSGV8W9LpDxBEJCfn4/4+HgcO3YMt27dQkhICEJCQjB69Gi4u7tD\nIpForP66ujpkZWXhm2++QUpKCq5fv47AwEBMnToVP//5z2FjY6Oxuql97E+JiOSY3JDO8G9LpLrH\njx8jISEBSUlJyMjIQGlpKdzc3ODs7Aw3Nzf4+/vD1tYWNjY2sLKyeqH8y6aCFgQBlZWVKCsrQ1lZ\nGW7evImzZ8/i1q1b4vV9fHwwefJkhISEoGfPnlq6W2oP+1MiIjkmN6Qz/NsSqV9tbS2ys7Nx5coV\nZGRk4P79+ygrK0NpaSlMTExgZ2cHa2trNDc3w9TUFKamprC0tER1dTWkUilqamrw8OFD9OjRA9bW\n1nBwcICTkxN8fHzg4+MDNzc3DjfrgtifEhHJ6UVyQ4aLH8ZE2iEIAp4+fYqysjI8evQITU1NaG5u\nhkwmg5mZGSQSCSwsLDBo0CDY2NjAwsJC1yGTEpjcEBHJdfnkhoiIiDrG5IaISK6z/aF2p4ImIiKF\nFRcXIygoCG5ubnB3d0d0dDQAYO7cufDy8oKXlxeGDRsGLy8vscymTZvg5OQEZ2dnJCUliftPnjwJ\nDw8PREREaP0+iIiItMVM1wEQEdHLSSQSbN++HZ6enqitrYWPjw9CQ0Nx+PBh8Zy1a9eiT58+AOQL\nbh4+fBh5eXkoLS1FSEgICgoKYGJigri4OGRlZeHPf/4zcnNz4ebmpqvbIiIi0hg+uSEi6qIGDRoE\nT09PAIClpSVcXFzw4MED8bggCDhy5Ajmz58PAIiPj8f8+fMhkUgwdOhQODo64vLlywCA1tZWNDY2\nQiqVciIBIiIyWExuiIj0QFFREbKysuDv7y/uu3TpEqytrTF8+HAAwIMHD2Bvby8et7e3R2lpKQBg\nyZIlCAgIQLdu3eDk5KTd4I3A6dOn4ezsDCcnJ2zZsuWl5/z2t7+Fk5MTPDw8kJWVpeUI9ceFCxd0\nHYLOGXsbGPv9A2wDVTC5ISLq4mpraxEWFoYdO3bA0tJS3H/o0CEsWLCgw7LPZqoMCQlBZmZmu1+8\nqfNkMhk++OADnD59Gnl5eTh06BBu3rzZ5pyEhATcuXMHBQUFiImJwfLly3UUbdfHL3VsA2O/f4Bt\noAomN0REXVhzczNmz56NRYsWYcaMGeL+lpYWHD9+HHPnzhX32dnZobi4WNwuKSmBnZ2dVuM1Runp\n6XB0dMTQoUMhkUgwb948xMfHtznnm2++wXvvvQcA8Pf3R3V1NR49eqSLcImIDBqTGyKiLkoQBISH\nh8PV1RWrV69ucyw5ORkuLi6wtbUV902bNg1fffUVmpqaUFhYiIKCAvj5+Wk7bKNTWloKBwcHcfv5\n4YAdnVNSUqK1GImIjAVnSyMi6qJSUlIQGxuLUaNGidM9b9q0CRMnTsThw4fFiQSecXV1xZw5c+Dq\n6gozMzPs3r2bCyhrgaJt/NP1Gtorx78ZsHHjRl2HoHPG3gbGfv8A26CzmNwQEXVRb7/9NlpbW196\n7J///OdL92/YsAEbNmzQZFj0Ez8dDlhcXNxmYoeXndPekEEu4ElEpBoOSyMiIlKBr68vCgoKUFRU\nhKamJhw+fBjTpk1rc860adPwr3/9CwCQlpaGPn36wNraWhfhEhEZND65ISIiUoGZmRl27tyJCRMm\nQCaTITw8HC4uLti7dy8AYOnSpZg8eTISEhLg6OiIXr16tfvkjYiIVMMnN0RERCqaNGkS8vPzcefO\nHURGRgKQJzVLly4Vz9m5cyfu3LmDnJwclJeXG/26OK9aGyguLg4eHh4YNWoU3nrrLVy7dk0HUWqO\nImsjAUBGRgbMzMxw7NgxLUanHYq0wYULF+Dl5QV3d3cEBgZqN0AteFUbVFRUYOLEifD09IS7uzsO\nHDig/SA16P3334e1tTVGjhzZ7jlK94UCERERaU1LS4swfPhwobCwUGhqahI8PDyEvLy8Nuf85z//\nESZNmiQIgiCkpaUJ/v7+ughVYxRpgx9++EGorq4WBEEQEhMTDaoNFLn/Z+cFBQUJU6ZMEb7++msd\nRKo5irRBVVWV4OrqKhQXFwuCIAiPHz/WRagao0gbfPTRR8L69esFQZDff79+/YTm5mZdhKsRFy9e\nFK5evSq4u7u/9Hhn+kI+uSEiItIiroujWBuMGTMGr732GgB5GxjS1NmK3D8A/O1vf0NYWBgGDBig\ngyg1S5E2OHjwIGbPni1O0NG/f39dhKoxirSBjY0NampqAAA1NTV4/fXXYWZmOG+VBAQEoG/fvu0e\n70xfyOSGiIhIi7gujmJt8Lx9+/Zh8uTJ2ghNKxT9byA+Ph7Lly8HYHhThCvSBgUFBaisrERQUBB8\nfX3x5ZdfajtMjVKkDSIiIpCbmwtbW1t4eHhgx44d2g5TpzrTFxpO6kdERKQH1L0ujj5S5l7Onz+P\n/fv3IyUlRYMRaZci97969Wps3rwZJiYmEATB4KYJV6QNmpubcfXqVZw7dw5SqRRjxozB6NGj4eTk\npIUINU+RNvjLX/4CT09PXLhwAT/++CNCQ0ORk5MDKysrLUTYNSjbFzK5ISIi0iJ1roujrxRpAwC4\ndu0aIiIicPr06Q6HrugbRe7/ypUrmDdvHgD5S+WJiYmQSCQvTDOurxRpAwcHB/Tv3x8WFhawsLDA\n2LFjkZOTYzDJjSJt8MMPP+D3v/89AGD48OEYNmwY8vPz4evrq9VYdaUzfSGHpREREWkR18VRrA3u\n37+PWbNmITY2Fo6OjjqKVDMUuf+7d++isLAQhYWFCAsLw549ewwmsQEUa4Pp06fj+++/h0wmg1Qq\nxeXLl+Hq6qqjiNVPkTZwdnZGcnIyAODRo0fIz8/HG2+8oYtwdaIzfSGf3BAREWkR18VRrA0+/vhj\nVFVVie+cSCQSpKen6zJstVHk/g2dIm3g7OyMiRMnYtSoUTA1NUVERIRBJTeKtMGGDRuwePFieHh4\noLW1FVu3bkW/fv10HLn6zJ8/H9999x0qKirg4OCAjRs3orm5GUDn+0ITwdAGcRIRERERkVHisDQi\nIiIiIjIITG6IiIiIiMggMLkhIiIiIiKDwOSGiIiIiIgMApMbIiIiIiIyCExuiIiIiIjIIPwvYo+V\nrDJiK4QAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 75
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise: Function plots\n",
"\n",
"Create a figure with a 2:1 aspect ratio, containing two subplots, one above the other. The **top** figure should plot one full cycle of a sine wave, that is $y=sin(x)$. Use $0$ to $2\\pi$ as values on the X axis. On the same scale, the **bottom** figure should plot $y=sin(x^2)$ instead. Tweak your figure until you think it looks good."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# EXERCISE 9: Plot y=sin(x) and y=sin(x^2) in two separate subplots, one above the other\n",
"# Let x range from 0 to 2*pi"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 76
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Finer figure control\n",
"\n",
"If you are not satisfied with the output of these general plotting functions, despite all the options they offer, you can start fiddling with the details manually.\n",
"\n",
"First, many figure elements can be **manually added** through top-level or Axes functions:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# This uses the result of the exercise above\n",
"# You have to copy-paste it into the same code-box, before the fig.show()\n",
"\n",
"# Add horizontal lines\n",
"ax0.axhline(0, color='g')\n",
"ax0.axhline(0.5, color='gray', linestyle=':')\n",
"ax0.axhline(-0.5, color='gray', linestyle=':')\n",
"ax1.axhline(0, color='g')\n",
"ax1.axhline(0.5, color='gray', linestyle=':')\n",
"ax1.axhline(-0.5, color='gray', linestyle=':')\n",
"\n",
"# Add text to the plots\n",
"ax0.text(0.1,-0.9,'$y = sin(x)$', size=16) # math mode for proper formula formatting!\n",
"ax1.text(0.1,-0.9,'$y = sin(x^2)$', size=16)\n",
"\n",
"# Annotate certain points with a value\n",
"for x_an in np.linspace(0,2*np.pi,9):\n",
" ax0.annotate(str(round(sin(x_an),2)),(x_an,sin(x_an)))\n",
" \n",
"# Add an arrow (x,y,xlength,ylength)\n",
"ax0.arrow(np.pi-0.5,-0.5,0.5,0.5, head_width=0.1, length_includes_head=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 77,
"text": [
"<matplotlib.patches.FancyArrow at 0x64eaf90>"
]
}
],
"prompt_number": 77
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Second, all basic elements like lines, polygons and the individual axis lines are customizable **objects in their own right**, attached to a specific Axes object. They can be retrieved, manipulated, created from scratch, and added to existing Axes objects."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# This uses the result of the exercise above\n",
"# You have to copy-paste it into the same code-box, before the fig.show()\n",
"\n",
"# For instance, fetch the X axis\n",
"# XAxis objects have their own methods\n",
"xax = ax1.get_xaxis()\n",
"print type(xax)\n",
"\n",
"# These methods allow you to fetch the even smaller building blocks\n",
"# For instance, tick-lines are Line2D objects attached to the XAxis\n",
"xaxt = xax.get_majorticklines()\n",
"print len(xaxt)\n",
"\n",
"# Of which you can fetch AND change the properties\n",
"# Here we change just one tickline into a cross\n",
"print xaxt[6].get_color()\n",
"xaxt[6].set_color('g')\n",
"xaxt[6].set_marker('x')\n",
"xaxt[6].set_markersize(10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<class 'matplotlib.axis.XAxis'>\n",
"12\n",
"k\n"
]
}
],
"prompt_number": 78
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# This uses the result of the exercise above\n",
"# You have to copy-paste it into the same code-box, before the fig.show()\n",
"\n",
"# Another example: fetch the lines in the plot\n",
"# Change the color, change the marker, and mark only every 100 points for one specific line\n",
"ln = ax0.get_lines()\n",
"print ln\n",
"ln[0].set_color('g')\n",
"ln[0].set_marker('o')\n",
"ln[0].set_markerfacecolor('b')\n",
"ln[0].set_markevery(100)\n",
"\n",
"# Finally, let's create a graphic element from scratch, that is not available as a top-level pyplot function\n",
"# And then attach it to existing Axes\n",
"# NOTE: we need to import something before we can create the ellipse like this. What should we import?\n",
"ell = matplotlib.patches.Ellipse((np.pi,0), 1., 1., color='r')\n",
"ax0.add_artist(ell)\n",
"ell.set_hatch('//')\n",
"ell.set_edgecolor('black')\n",
"ell.set_facecolor((0.9,0.9,0.9))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<a list of 3 Line2D objects>\n"
]
}
],
"prompt_number": 79
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise: Add regression lines\n",
"\n",
"Take the scatterplot from the first example, and manually add a regression line to both the R-G and the R-B comparisons. Try not to use the plot() function for the regression line, but manually create a Line2D object instead, and attach it to the Axes.\n",
"\n",
"Useful functions: \n",
"- **np.polyfit(x,y,1)** performs a linear regression, returning slope and constant \n",
"- **plt.gca()** retrieves the current Axes object \n",
"- **matplotlib.lines.Line2D(x,y)** can create a new Line2D object from x and y coordinate vectors"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# EXERCISE 10: Add regression lines\n",
"import numpy as np\n",
"from PIL import Image\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.lines as lines\n",
"\n",
"# Open image, convert to an array\n",
"im = Image.open('python.jpg')\n",
"im = im.resize((400,300))\n",
"arr = np.array(im, dtype='float')\n",
"\n",
"# Split the RGB layers and flatten them\n",
"R,G,B = np.dsplit(arr,3)\n",
"R = R.flatten()\n",
"G = G.flatten()\n",
"B = B.flatten()\n",
"\n",
"# Do the plotting\n",
"plt.figure(figsize=(5,5))\n",
"plt.plot(R, B, marker='x', linestyle='None', color=(0,0,0.6))\n",
"plt.plot(R, G, marker='.', linestyle='None', color=(0,0.35,0))\n",
"\n",
"# Tweak the plot\n",
"plt.axis([0,255,0,255])\n",
"plt.xlabel('Red value')\n",
"plt.ylabel('Green/Blue value')\n",
"\n",
"# Fill in your code...\n",
"\n",
"# Show the result\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 80
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scipy\n",
"\n",
"**Scipy** is a large library of scientific functions, covering for instance numerical integration, linear algebra, Fourier transforms, and interpolation algorithms. If you can't find the equivalent of your favorite MATLAB function in any of the previous three packages, Scipy is a good place to look. A full list of all submodules can be found [here](http://docs.scipy.org/doc/scipy/reference/).\n",
"\n",
"We will pick two useful modules from SciPy: **stats** and **fftpack**\n",
"\n",
"I will not give a lot of explanation here. I'll leave it up to you to navigate through the documentation, and find out how these functions work."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Statistics"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import scipy.stats as stats\n",
"\n",
"# Generate random numbers between 0 and 1\n",
"data = np.random.rand(30)\n",
"\n",
"# Do a t-test with a H0 for the mean of 0.4\n",
"t,p = stats.ttest_1samp(data,0.4)\n",
"print p\n",
"\n",
"# Generate another sample of random numbers, with mean 0.4\n",
"data2 = np.random.rand(30)-0.1\n",
"\n",
"# Do a t-test that these have the same mean\n",
"t,p = stats.ttest_ind(data, data2)\n",
"print p"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.185717962018\n",
"0.355728186743\n"
]
}
],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import scipy.stats as stats\n",
"\n",
"# Simulate the size of the F statistic when comparing three conditions\n",
"# Given a constant n, and an increasing true effect size. \n",
"true_effect = np.linspace(0,0.5,500)\n",
"n = 100\n",
"Fres = []\n",
"\n",
"# Draw random normally distributed samples for each condition, and do a one-way ANOVA\n",
"for eff in true_effect:\n",
" c1 = stats.norm.rvs(0,1,size=n)\n",
" c2 = stats.norm.rvs(eff,1,size=n)\n",
" c3 = stats.norm.rvs(2*eff,1,size=n)\n",
" F,p = stats.f_oneway(c1,c2,c3)\n",
" Fres.append(F)\n",
"\n",
"# Create the plot\n",
"plt.figure()\n",
"plt.plot(true_effect,Fres,'r*-')\n",
"plt.xlabel('True Effect')\n",
"plt.ylabel('F')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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XA1VVbAbqgRm/+hWHjx5lwbx5+sXGdTmujAxyVRXF4SBr4EBmDhrE2uefJxV4MBAws4TK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}
],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import scipy.stats as stats\n",
"\n",
"# Compute the pdf and cdf of normal distributions, with increasing sd's\n",
"# Then plot them in different colors\n",
"# (of course, many other distributions are also available)\n",
"x = np.linspace(-5,5,1000)\n",
"sds = np.linspace(0.25,2.5,10)\n",
"cols = np.linspace(0.15,0.85,10)\n",
"\n",
"# Create the figure\n",
"fig = plt.figure(figsize=(10,5))\n",
"ax0 = fig.add_subplot(121)\n",
"ax1 = fig.add_subplot(122)\n",
"\n",
"# Compute the densities, and plot them\n",
"for i,sd in enumerate(sds):\n",
" y1 = stats.norm.pdf(x,0,sd)\n",
" y2 = stats.norm.cdf(x,0,sd)\n",
" ax0.plot(x,y1,color=cols[i]*np.array([1,0,0]))\n",
" ax1.plot(x,y2,color=cols[i]*np.array([0,1,0]))\n",
"\n",
"# Show the figure\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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22G2EPxsOlPeA+vT6p2x4bANQHnS9Ef0Gs9xn4WzqDMCs9Fl0t+xO\nf6v+9+fGRZUkiBJ6dafCcknniZrQv39/+vfX/uEyadIkzZ/r1q3Lrl27HvSyRC2UpcrCzsZO8zox\nPREneydNeq+ktITguGBaebcCygOe/df289+n/qv5mi1Xt/BW17c0r1dcXsGEVhM09XtLQpbwYsMX\nqWNeXne1Km4VTWya8Ljj4+Vfn7GF1JJUXnd5HYAzBWfYpNrEVe+KTuXiwZEgSujVnVocSDpPCFGb\nZauytdJ5scmxeNX30rwOTQjFo64H1hbWAESlRVFcWkwTl/LO4qm5qVy6cYmn/J4C4GbhTX6P/J1v\nen0DQG5JLsvDlnNq4CkACtWFfHb9M7a13wZAUVkR78W+x0rflRgbGFOilDA+ZTwLnBbI03g1RGqi\nhF5VtxNVVFiIoigPeFVCCHHvsnKytNJ5fw2iLkZr10MdDDlI76a9NbtM24O3079xf8xNyn/RXH9t\nPf0b9KeuZXkAtDp8Nd3rd6ehbXnLhB9if+Ax+8do71BelL44aTHNLZvTy668aeeCmwtwNXaVppo1\nSHaihF7d6dgXIyMjjI2NKSkuxtTMrNI5QgjxsPprOi82ORbv+t6a138tKj9y/Qg9/XtqXm+5soUJ\nHSsKyNdcWcPn3T4HQF2m5uvgr9nQvbz2Kb80n3nh8wjoFABAekk6XyZ+ydHmRwGIKI7gf5n/46zX\nWWmqWYNkJ0roVdEdCstBUnpCiNqrunReZUFUV7+uAOQU5nAy7iT9GvUDICQjhBu5N+jtVf7E3e6E\n3TiZO9GpXnmB+dKYpTzh+ASt7Mrrqz6J/4Tn6z6Pv6U/iqIwKWUSHzh+gI+Jz/29aXFHshMl9Kqo\nsBAbe/sqr996Qs/2DnOEEOJhlKXK0upWHpscy/Aew4HyIvJL0Zc0QVRseizFpcX4OfsBsC98H128\nu2BtVl4vtS54HS80fUFzpMuSa0t4o0n5uXkF6gIWRC5gX+d9AFwvuM6G9A2EtCk/93G9aj03y27y\npsObD+CuxZ1UuxP16quv4uzsTIsWLSq9vn79elq1akXLli3p0qULly9Lo69/szu1OIDynagi2YkS\nQtRCOjtRKRU7UdEp0VibW+Nk5wSU70J1a9RNk2rbdW0XA/0HAuWHDa+/tp7RzUYDEJoVSlBmEM/5\nlB80vCpuFR0dOtLCtvzn7nux7/Gu27s4mTiRo87h/fT3WVpvKcYGsg9S06oNosaOHUtAQECV1xs0\naMCRI0e4fPkyH374IRMnTtTrAkXtcqfCcqgoLhdCiNomK6eiJqqsrIy45Dg8nT0BCIoO0jov78j1\nI3RtVJ7KU5ep2RO2h4FNyoOoI/FHsDOzo1W98lTd0tClTGg8ATMjM4rLipkfMZ8ZfjMAOJx9mEt5\nl3jTpXzXaXbGbPpZ9qOTRacHc9PijqoNY7t27UpMTEyV1zt37qz5c8eOHUlISNDLwkTtdKcWByAN\nN4UQtVd2bkWzzdSbqVhbWmNlYQXA1birtPCqyNgcuX6Eqb2nAnAq7hSutq54OZTvWq0LXqfZhVKV\nqPg54mcuDy3P4qxPWE8j60Z0cOhQfuhw7Lt87vk55obmXC26ys+qnwn2Cn5g9yzuTK97gatWrWLA\ngAHVTxSPrOp2oswlnSeEqKWyciqOfflrUfnVuKsM7lB+pFBydjKpqlSauzUHYFfILgY1GQRAQUkB\n28K3cbVreXPMdRHr6O3aG3crd9SKmi/Cv2B5q+UAbM/cTolSwsi6I1EUhampU/m4zsc4GTs9sHsW\nd6a3IOrPP//kxx9/5Pjx41XOkVPQH313anEAYCrpvEfaPzkFXYjaIluVrUnnxSbH4uV8WxAVe5UZ\nz5an4I6FH6OLbxdN0fjvIb+z8tmVAOyM2Em7+u1wtXZFURSWhCxhWedlAGy5sYW6pnXpXqc7akXN\nrLhZzPeej6GBIRtzNpJdls1ku8kP8pZFNfQSRF2+fJkJEyYQEBCAg0PVhx/KKeiPvupaHEg679H2\nT05BF6K2uP0A4tt3oopKiohKicLf3R+oKCoHiM6MJi0vjQ7uHQBYd60ilXco6RBGBkZ0q98NRVH4\nPPxzPmvyGQYGBqxPXY+DsQP97fujKlPxbvq7bHbZjJGB0YO+bXEH99wnKi4ujmHDhvHzzz/j6+tb\n/ReIR1pRYSFmks4TQjyCbi8sv/3JvLDEMHycfTAzKW8ifHsQtStkF0/7P42hoSEZBRkcjT/KUL+h\nACwNWcpr/q9hYGDA7pTdAAyoN4DismI+jv+Yzz0/x8DAgE8yPuFJyyd53OLxB33LohrV7kSNGjWK\nw4cPk56ejoeHB3PmzKGkpAQoP8Tzk08+4ebNm0yZMgUAExMTzpw5c39XLR5a1bU4kHSeEKI2Kiou\nolRdiqWFJVC+E/VkuyeB8lRec8/y+qes/CwiUyNp69UWKE/lTe5UnoLben0rfX36Ym1qTUpBCoeS\nDrG662oAvoz4kg/8PsDAwICVySvxt/Cnm103wovDWZ29mmBvKSZ/GFUbRG3cuPGO11euXMnKlSv1\ntiBRu91NYbmk84QQtU22Khs7aztN36fb03lX4yqCqFORp2jn3Q5TY1PyivM4GXuS3176DYDNoZuZ\n3Lo8oFoTvoZhXsOwNbXl9M3TJBQmMNxlOPnqfOYmzGVXk10AvJf+Hu86vouzsfODvmVxF+TYF6FX\nxYWF1bY4kHSeEKK2yVZlax0+HJdS0SPqauxVmnuVB1EnI0/SuWF565/DUYdp69YWW3NbUvNSOZt8\nlgENBlCmlLHy+komNC4/R29B5ALe9HkTY0NjliQvoYttFx6zfow/8//kUuEl3rSXzuQPKwmihF4V\nFhTccSdK0nlCiNro9sOHc/NzKS4pxtG2/AiYK7FXNDtRJyNP0qlheSPMvdf30rdRXwC2XN/CgAYD\nsDCxIDApEHMjczo6dSQ6L5pD6YcY5zWO7NJs/pf4Pz7x+AS1ouattLf40ulLzA2r/sVU1CwJooRe\nVdfiQNJ5Qoja6PYjXxLTEnGv546BgQGqAhUpWSk0rN+QsrIyTkefplODiiCqX+PyA4c3hW5ihP8I\nAFZcX8HExhMxMDDg2+hvGec5DhtjGxYlLaK/Q3+aWDZhbc5aLA0sec76uZq5YXFX5OAdoVd30+Ig\nKyPjAa5ICCHuXbYqG1trWwAS0hJwc3ID4FrcNZp4NMHIyIjgxGCcbJyoZ1uPmMwYbhbcpLVLa5Jy\nkwhKDaKvT1/SC9P5I+EPlnZeSlZJFmvj13K5x2VySnNYlLSIEy1OkFuWy6yMWWxz2aapwRIPJ9mJ\nEnqjVqspLS3FxNS0yjmSzhNC1EaqPBU2VjbA/+9EObkD2kXlt9dD7b2+lz5+fTA0NOS3sN8Y5DsI\nc2Nz1kWsY5DHIBzMHFgeu5ynnZ/G3cKdxcmL6efQDz8LP77K/IqeFj3pYNGhZm5W3DXZiRJ6cyuV\nd6ffnCSdJ4SojXLzczVB1O07UVdir2gVld9K5QVcD2B48+FAeSrvg04foCgKK66v4PvHv6e4rJhF\nUYv4vePvqNQqvr3xLUdbHCW+JJ7vsr7jotfFGrhL8XfJTpTQm+raG4AEUUKI2kmVp8La0hqofieq\nRF3Cn5F/8lSjp0hQJRCSEUIf7z4cTz1OmVJGV+eubErchL+1P63tWrMkaQl97PvQ2KIxM9NnMsV+\nCp4mnjV2r+LuyU6U0Jvq2hsAmEmLAyFELXR7Oi8hNYE+7fsAEBIfQlOPptzMu0l8Zjwt3VtyMu4k\nDRwbUM+6Hl+f/ZohfkMwNTJl1fVVjG80HoCFkQv5vMnn5Kpz+SbpGwKbBXK56DL78vcR7hNeY/cp\n/h7ZiRJ6U117AwAzqYkSQtRClaXzsvOyycrLwqOuB2eiz/CY92MYGxkTEBag1drg2UbPkluSy/bY\n7Yz2Hc2xzGPkq/PpW68v3yV/Ry+7XjSxbMIH6R8w03EmNoY2NXmr4m+QIEroTXXtDUDSeUKI2kmV\np8LaSjudF5YYRmO3xhgaGmrVQ91qbZCcm0xwejC9vXqzNXYrXZy74GzhzOLoxUz1mUp+WT4Lbyzk\nQ/cPOZx/mJDiECbZT6rJ2xR/kwRRQm+qa28Aks4TQtROt9J5xSXFZOZkUs+hHqGJoTRxbwJU1EOl\n5aYRnhFOZ8/O7IjYQb8G/TAzNmNN+Bpe8X2FhIIEDqQdYIzHGJYlL6OHbQ+aWDTh/fT3+bTOp5ga\nVP10s3j4SBAl9KaosBCzu0jnyU6UEKK2uZXOS8pIon6d+hgZGRESH4K/u395k82o03Ru2JlDkYfo\n5tMNU2NTtoVvY5jfMGJzY7mceZlBnoP4PuZ7XnR/ERNDExbeWMgs91lsz91OoVLIKJtRNX2b4m+S\nIErozd2m86QmSghR29x6Oi8hNUHzZF5oYij+7v6EJYfhaOVIPdt6HIw4SG/f3mQVZnEi8QT9G/Rn\nXcQ6nvd5HgWFFXErmOozlTVpa2hv3V5TC/VF3S8wNJAfybWN/BcTenO3heXFEkQJIWoZVW55Ou/2\nHlGhCeXpvLMxZ2nv0x6AQ5GH6NWwF7ujdtPDowdWJlb8FPETr/i9wuYbm2lt25qGVg2ZnzifD9w/\nYE3OGlyMXehn2a8mb0/8QxJECb256xYHEkQJIWqZW+m8W0XlJaUlRKdE4+viy9nos7T3bk/szVhy\nCnNo7tycbde3MbTRUE6knsDYwJh2ddqxOHoxb/i8web0zXiYedDKqhWzM2Yzr+48Od6llpIgSujN\n3e5ESU2UEKK2uVVYnpBavhMVmRyJex13zE3NORdzjvY+7ct3oXx7UaQuYn/MfgY1HMRPET8xxm8M\nZ7LOkFmcSb96/ZiXOI8P3D5gcdZiOpp3pKNFx5q+PfEPSRAl9EZqooQQj6pbNVGJaYm413MvT+V5\nNKGktITLCZdp69WWgxEH6dWwF/ti9vFY/cewMrXit+jfGN1wNIujF/O6z+sEZAVgZGBER5uO/O/m\n//is7mc1fWviHkgQJfTmblocmJiaUlpSglqtfkCrEkKIe1NSUkKpuhQLcwsS0soLy0MSQvB38+dq\n4lW86nhhbWatKSrfFr6NoX5D2R67nfZO7TE0MmRP6h5e9XyVeYnzmO42na+zvmag1UD8Tf1r+vbE\nPZAgSujN3bQ4MDAwwNTMjOKioge0KiGEuDe5+blYW1pjYGCgSeeFJpQ/mXerqDw0LRQzYzM87DzY\nFbGLZ/yeKU/l+Y5heexyRriO4ErBFZJLkulp35Pvsr7jozof1fStiXskQZTQm7tJ54F0LRdC1C63\nUnllZWUkZybjWtdV02jzXMw52nu316TyjiYepaF9Q4yNjTmddpqBHgNZEbuCKd5TmJcwj/dc32Ph\nzYU8a/MsPiY+NX1r4h5JECX05m4Ky0HaHAghapdbT+al3kzF3toeUxNTQhNCaezWWLMTdSjiEL19\ne7M9fDvP+D3DL1G/MMRzCIGZgXhYeIAxXMy7SD/HfizPXs4sx1k1fVtCDySIEnpzNy0OQNocCCFq\nl1tP5iWmJeLm5EbSzSTMTMywNLckLDmM5m7NCYwKpGeDnvwe+TuDfAexIWoDLzZ8ke9jvmey92S+\nTPyS/7r+l2+yvuFF2xfxMPGo6dsSeiBBlNCbv7MTJek8IURtcfuTea51XTVNNoPig/Cv709IWggu\nti7klOZQWlaKqYkp8Xnx+Nj5cCbrDB0cO7Avax+D6wxmTc4aZjjOqOlbEnoiQZTQm79TEyU7UUKI\n2uL2c/Nc6rjoFJUfivj/LuWRu3m6wdNsjN7ICJ8RrI5fzWj30Xyf+j3jncezOGcxY+3G4mLsUtO3\nJPREgiihN3cbRJmam1MkO1FCiFriVjrvVhB1q73BrU7lt1ob7InaQ/8G/dkQuYHnfZ5nVdwqRriP\nYF3aOobVHcbGnI287/B+Td+O0CMJooTeFBUUVNviAMrTebITJYSoLVR5KqytrMuDqLraO1GtPFpx\nIvYEbdzacCbpDI7WjpQpZSSWJuJv7c/hvMMMdBjIytyVTLSbSD3jejV9O0KPqg2iXn31VZydnWnR\nokWVc6ZNm4afnx+tWrXi4sWLel2gqD2kxYEQ4lGUm6edzrt+4zqudVyJy4ijSCnCt64v51PP87jb\n42yN28oLDV/gh9gfGO81nkVJixhRbwRbVFt4x/Gdmr4VoWfVBlFjx44lICCgyut79uwhIiKC8PBw\nli9fzpQpU/S6QFF7SIsDIcSj6FY6LzkjGQdbB1KyUkjPTy/fhYo7QTefbuyO3E0/n378EvULj9d/\nnKuqqxQZF9HUoim/FvzKVPup1DGqU9O3IvSs2iCqa9euODg4VHl9586djBkzBoCOHTuSlZVFSkqK\n/lYoag1pcSCEeBTdejovKSOJYnUxPs4+XIi7QHuf9hyJPkJX767sidpDHds6uFq6cuDmAca4j2FR\n8iJGOY/i99zf+a/Df2v6NsR9YHyvb5CYmIiHR0W/C3d3dxISEnB2dtaZO3v2bM2fe/ToQY8ePe71\nrxcPEWlxIAIDAwkMDKzpZQihV7n5uVhZWpF6M5Wsgiz8XPw4F3OOAS0GsHb/Wv7T/T/YmNoQmBrI\nc97PsSB+AQtaL2BP0h5OlZ5iiv0U7I3sa/o2xH1wz0EUgKIoWq8NDAwqnXd7ECUePdLiQPz1l6M5\nc+bU3GKE0BNVngoDAwNsLG2ITo3Gz9WPneE7GdF5BM7WzpxKPkXfBn35OfZn5nSaQ1u7tmzI3MBY\nl7F8lvsZ132u1/QtiPvknp/Oc3NzIz4+XvM6ISEBNze3e31bUQvdzQHEIC0OhBC1iypPRZG6SFNU\n7uHkQVJ2EvGqeLr5dGNP1B4cbRxp7diaX5N/ZYDrAC7nXSbGIIaxdmOpa1S3pm9B3Cf3HEQNHjyY\ntWvXAnDq1Cns7e0rTeWJR19RQcFd7URJiwMhRG2Sm59LYUkhLnVcCL8RDsbQwq0Fx2KO0catDcHp\nwVzOuUwv915E5kVysfgiY+qPYb1qPW87vF3Tyxf3UbVB1KhRo3j88ccJCwvDw8ODH3/8kR9++IEf\nfvgBgAEDBtCgQQN8fX2ZNGkSS5cuve+LFg8naXEgHkYBAQH4+/vj5+fHl19+WemcwMBA2rRpQ/Pm\nzaVWU+hQ5anIL8rHpW55EJVVmEVrz9YciT5CsVExT3g8wcGkg6SQwnC34ey8uZNC00JG2ozE1di1\nppcv7qNqa6I2btxY7ZssWbJEL4sRtZeiKHcdREmLA/GgqNVqpk6dyoEDB3Bzc6N9+/YMHjyYJk2a\naOZkZWXx+uuvs3fvXtzd3UlPT6/BFYuHkSpPRU5+Do52jmTlZRGVEUUj10aYJ5hzJvkMTvZOdLft\nzihbnQoAACAASURBVOYbmxnuO5xhFsNYq1rLea/zNb10cZ9Jx3KhFyXFxRibmGBoWP3/UtLiQDwo\nZ86cwdfXF29vb0xMTBg5ciQ7duzQmrNhwwaGDx+Ou7s7AHXrSv2K0Jabn0tWXhbGJsb4uvhyMe4i\nRRTxhPcT7I3ZS1xRHL51fWls05jfMn/DxsqGQdaD8Dbxrumli/tML0/nCVFUWHhX7Q1AWhyIB6ey\nFiynT5/WmhMeHk5JSQk9e/ZEpVLx5ptvMnr0aJ33khYt/16qPBWZOZnYOtrSoH4D9sXvIzo7Go86\nHjgXOHMu4xzGDsb4Ovpib2DP+rz1HPU4WtPLFn/TP2nRIkGU0IvCuywqB2lxIB6cqtqt3K6kpIQL\nFy5w8OBB8vPz6dy5M506dcLPz09rnrRo+XdSq9UUFBaQlpVG3eK62Nna0ci5Ecdij9HPoR/e9bzx\nNPPkVNYp4s3i6Vm/J5ZY0ti0cU0vXfxN/6RFi6TzhF7cbT0USIsD8eD8tQVLfHy8Jm13i4eHB089\n9RQWFhbUqVOHbt26ERQU9KCXKh5SeQV5WFlakZSRREZuBoqxQiPXRuQX53M6+TTp6nRsbGx4wvkJ\njA2N2V6wnRl1ZtT0ssUDIkGU0IuigoK76hEF0uJAPDjt2rUjPDycmJgYiouL2bRpE4MHD9aaM2TI\nEI4dO4ZarSY/P5/Tp0/TtGnTGlqxeNjcfuTLjZs3yC7KxsLSgvae7YnJiSEkJ4QzqjNkG2fTyrEV\nj5k/RiuzVjW9bPGASDpP6MXf2YmSFgfiQTE2NmbJkiX07dsXtVrNuHHjaNKkiaZFy6RJk/D396df\nv360bNkSQ0NDJkyYIEGU0FDlqbC0tCRPySMiKYIiqyKwBVtbW/ws/TC3NyfNJI2QwhBiS2PZ5LSp\nppcsHiAJooRe/N3CcmlxIB6U/v37079/f62xSZMmab1+5513eOeddx7kskQtkZuXi7m5OfUs65FS\nmsL1lOvkW+XjZu5GgWkBpmam1LerT0PbhhSYFNDJolNNL1k8QBJECb0oKijA9C53oqTFgRCitlDl\nqTAxNcHMxgxTS1OKLItIzk3mRuoNqAOGeYYYmhviiCMr66ys6eWKB0yCKKEXfyedJy0OhBC1hSrv\n/9i77+ioqu2B49/JzKT3QnolIST0LqIIPJSiggUFnr2DomDF8nwCD0UU9an8VBSwi4ANeEBESlAR\nKQFpgSSkQHqvM5lMZnJ/fwxMGEFIgwTZn7Vcizk5984ZVgw7++yzbzUqtQoHRwfs3ezReGvwdfGl\n2LEYTz9P7Lzs0HpoUWlUDHMa1t7LFReYBFGiTTRnO09aHAghLhY1+hrLESw1KBoFtODg5AD2UKIq\noUHVgKPWkbe9325SSw3x9yJBlGgTzekTJS0OhBAXi2pdNWbFTK2pFkOdAbPeTK2mllJ1Ke4ad1wd\nXXFTuzHWZWx7L1W0AwmiRJswGgxNr4mSFgdCiItEta6a+oZ6DLUGisxFKBoFjYeGYO9gcAOc4Xnv\n5yULdYmSPlGiTTS3JqrOYEBRlPO8KiGEaJ0afQ21xloKawpxdnEmpFMIzq7O5JNPiboEjZ2Gm1xv\nau9linYiQZRoE4ba2ibXRKnVajQaDfVG43lelRBCtE61rhqdUQda8PL0ol5TT7m6HF8PXzzdPXne\n53nUKnV7L1O0EwmiRJtoTiYKpM2BEOLiUK2rpspYhYe7B3r05JpzcXV3pdi+mAa7Bia7TW7vJYp2\nJEGUaBPNqYkCaXMghLg4VNRUYFaZQQuFxkIc3B3QO+rxdPPkee/n0aq07b1E0Y4kiBJtojnbeSBt\nDoQQF4eSyhLsHe2pMFfg7uFOrUMtajc1JrWJu93vbu/liXYmQZRoE83dzpM2B0KIi0F5dTl29nY0\naBrQOepQeajQuGp4zuc5HO2a/jNP/D1JECXaRLNroqTNgRDiIlClr8KkNaF11WJyNaFx16BoFR70\neLC9lyY6AOkTJdpEXW0tDs3czpOaKCFER1dVW0Wdex1GByOKu4Kdmx1Pej2Ji51Ley9NdACSiRJt\noiWZKKNkooQQHZyuXoedsx12nnbgDRoHDY95PdbeyxIdhGSiRJuQFgdCiL+jOupQnBUafBpQeap4\n0ONBPNWe7b0s0UFIECXaRHO386TFgRCio1MUhQZNA3gAnUDtqOYFnxfae1miA5HtPNEmmpuJkhYH\nQoiOTqfXgQsQCPjALa634Kfxa+9liQ5EgijRJqTFgRDi76ZGXwPuQDionFQs8F/Q3ksSHYwEUaJN\nNLfZprQ4EEJ0dGWVZRAEBMLl9pcTpAlq7yWJDuacQVRCQgJdu3YlJiaG+fPnn/b1kpISRo8eTe/e\nvenevTuffPLJ+Vin6OCa+9gXaXEghOjoMvMyoSfgAkuDl7b3ckQHdNYgymw2M23aNBISEkhOTmbZ\nsmUcPnzYZs7ChQvp06cPf/zxB4mJiTz55JOYTKbzumjR8UiLAyHE382OtB3QFdwN7nRx6NLeyxEd\n0FmDqJ07dxIdHU1ERARarZZJkyaxatUqmzmBgYFUVVUBUFVVhY+PDxqNHPq71DR7O08Ky4UQHdyX\n5V+CO3wd/nV7L0V0UGeNdnJzcwkNDbW+DgkJYceOHTZzHnjgAUaMGEFQUBDV1dWsWLHiL+83a9Ys\n65+HDRvGsGHDWrZq0eGcKRN1ePduFj75JNG9evHom2/aBNcOjo5UlJZe6GWK8ywxMZHExMT2XoYQ\nbSJjYAboYYzXmPZeiuigzhpEqVSqc97glVdeoXfv3iQmJpKens7VV1/Nvn37cHNzO23uqUGU+Pto\naGjAVF+P1t7eOlZTWclzN9zAfbNmsfHrr/l83jzuefFF69elxcHf059/OZo9e3b7LUaIVqisrwQ/\nYAfQt71XIzqqs27nBQcHk52dbX2dnZ1NSEiIzZzffvuNW265BYDOnTsTGRlJSkrKeViq6KhOZqFO\nDbq/e+89+g4fzvX338+zixez4r//paqszPp1aXEghOjIxqePh3rw3+7f3ksRHdhZg6j+/fuTlpZG\nVlYWRqOR5cuXM27cOJs5Xbt2ZePGjQAUFhaSkpJCVFTU+Vux6HD+vJVnNptZ/eGH3DJ9OgCBEREM\nvOYaNnz1lXWOtDgQQnRU9Uo9W5WtkAPx2vj2Xo7owM4aRGk0GhYuXMioUaOIj49n4sSJxMXFsWjR\nIhYtWgTA888/z+7du+nVqxcjR47ktddew9vb+4IsXnQMfw6iDmzbhou7O3H9+1vHxt59Nxu+/NL6\nWlocCCE6qn8V/AvMwH6I9o9u7+WIDuycx+jGjBnDmDG2RXUPPfSQ9c++vr6sWbOm7VcmLhp1tbU2\nPaK2/e9/XDF+vM2cPsOGkZWcTHlxMV5+ftLiQAjRIZkVM29XvA3FQB6ExIec8xpx6ZKO5aLV6gwG\nm/YG29et4/Jrr7WZY+/gQL8RI9j544+AtDgQQnRMSyqWUNdQB1lAKQT5Spdy8dckiBKtdup2XmlB\nASV5ecQNGHDavIGjRrHrp58AS02UbOcJIToSRVF4ofgFqACKgGrw8fBp72WJDkyCKNFqdbW11iDq\n4PbtdB88GDu707+1el1xBfu3bQOkxYEQouP5oeYHSk2lkA9UgMaowc3l9HY9QpwkrcVFq9UZDDic\n2M47+NtvdB88+IzzIuLjqSotpaywUFocCCE6FEVReLroaagGVZkKlV6FxqzB1dm1vZcmOjDJRIlW\nO3U77+D27fS4/PIzzrOzs6P75Zezf9s2aXEghOhQNus3c9x4HHWpGk2NBnWtGrVZLZkocVYSRIlW\nM5zYzqs3Gkndu5e4gQP/cm7cgAGkJCVJiwMhRIcys3gm6EBVqcJUbcJUbQIjEkSJs5IgSrSa8cR2\nXlZyMoERETi7/nX6u0ufPqTu3SstDoQQHcb22u0k1yXTUN6AtkaLi8kFR5Uj5nqzbOeJs5IgSrTa\nye28o/v3E92r11nnxvTpQ9revdLiQAjRYbxY8iImnQlnnTOKXsFYY8RF5UJdXZ1kosRZSRAlWu3k\ndl76/v107tnzrHP9Q0OpNxqpLCmxPLjYZLpAqxRCiNP9YfiDHbU7cKp2okHXgLPJGbt6OzSKBg0a\ntFptey9RdGASRIlWO9ls8+i+fUSfI4hSqVTE9O7N0X37pM2BEKLdzSmdg1lvhlrwNHtSVVWFr70v\npjoTzg7O7b080cFJECVazWgwYH9iO+9cmSiAmN69SfvjD2lzIIRoV8l1yWzSb8K3zheHWgeKy4vx\n0/phr9ij0+lwd3Zv7yWKDk6CKNFqhtpaGkwmTPX1+AUHn3N+RHw8xw4fljYHQoh2Na9sHk51TpRW\nllKvryfYPhgXXNDV6DDVmfBw8WjvJYoOToIo0Wp1BgO6sjJievVCpVKdc35EXBxZhw9LmwMhRLtJ\nN6azumY1dno7AgggVBOKul5NRVUFZRVleLt64+YqReXi7KRjuWg1o8FAbWkp4XFxTZofHhfHsSNH\ncAgLkzYHQoh2Ma9sHoENgSgmBZ1OR3VZNboSHVSDr4svHmoPOZknzkmCKNFqhtpaqsvK6HfFFU2a\n7+7lhaOzMxqNRrbzhBAX3PH643xT8w1KpYJdpR0OtQ5oFS2BLoE4mB3QVmmxt7OXIEqckwRRotXq\nDAYqi4oIi41t8jUR8fHkFhfLdp4Q4oJ7rfw1utAFO7UdKgcVamc11IOd2Q5MQD1oTPLcPHFuEkSJ\nVqszGCgvKCC0S5cmXxMRF0dOXp5kooQQF1S+KZ+vqr5CKVfQlmtxqXbBVeeKY60jTiYntIoWtVqN\nxl4jmShxTlJYLlqttroaXVUVAeHhTb4mPC4Ok8EgLQ6EEBfUgvIF9Nb0pqu2K8HaYMwmM8fKjpGS\nn0JhaSG1+lowg71atvPEuUkmSrRaTUUFvkFBqNXqJl8T3rUr9Xq9ZKKEEBdMsamYjys/xrvGm06G\nToSoQwhyD0Jv1lNoKiQrPYt6cz1hjmG4a91lO0+ck2SiRKvVVlYSEBHRrGtCY2Ko0+mkJkqcdwkJ\nCXTt2pWYmBjmz5//l/N27dqFRqPhu+++u4CrExfSfyv+y2D7wdg32JNcnszhosPU6+vpZN+JOL84\nYv1jyS3NpaKqApWikkyUOCcJokSrGWpqCIyMbNY1fiEhmOrq0FVVnadVCQFms5lp06aRkJBAcnIy\ny5Yt4/Dhw2ecN3PmTEaPHo2iKO2wUnG+lZvL+aDiA6qqq4jXxPMP73/Q0NDArtxdVFZV4qX1orNf\nZ0J8QygsK0QxKxJEiXOSIEq0mlGvb1ZROYBarcbJ3Z3SvLzztCohYOfOnURHRxMREYFWq2XSpEms\nWrXqtHnvvvsuEyZMwM/Prx1WKS6EhRULudLxStL16ewp2oOLyYXLfS8nyiOKXdm7qNXX4ufsR3RA\nNCUVJdTX1+PuKo99EWcnNVGi1cwGA+HNaG9wkpu3NyW5uedhRUJY5ObmEhoaan0dEhLCjh07Tpuz\natUqNm/ezK5du/6y6/6sWbOsfx42bBjDhg07H0sW50F1QzXvVLzDleYrGeM+hiRdEltytjDSayQD\n/QdSWVjJkbwjXBl2JSE+IXi5eaHT6yQTdYlJTEwkMTGxWddIECVaRVEUFJOJyO7dm32th58f5YWF\n52FVQlg05TFEM2bM4NVXX0WlUlm+n/9iO+/UIEpcXN6veJ8hjkPYkrOF4erhDPcazk9lP7Ezeydj\nQ8YyJHwIKxNX0s+3H76uvgT6BlKtq5Yg6hLz51+OZs+efc5rZDtPtEplaSkoCv4hIc2+1tPfn8qS\nkvOwKiEsgoODyc7Otr7Ozs4m5E/fq0lJSUyaNInIyEi+/fZbHn74YVavXn2hlyrOE32DnjfL38S/\n3p8bvW9kc9FmqqqrGBM0hpLaEo4VHyPSM5LoTtGk56fj5uhGgHeABFGiSSSIEq2Sk5aGGXBwdGz2\ntT6BgVSXlrb9ooQ4oX///qSlpZGVlYXRaGT58uWMGzfOZk5GRgaZmZlkZmYyYcIE3n///dPmiIvX\nB5UfcJnjZXxf9D2BSiDX+l/LmuNrcFFcGBs1ll+yfkFr1tI/oj8puSnYq+wJ9JFMlGiacwZRTTke\nnJiYSJ8+fejevbvUCVxijqWmglrdpG2TP/MNDkZXUXEeViWEhUajYeHChYwaNYr4+HgmTpxIXFwc\nixYtYtGiRe29PHGe6Rp0vF72Oj3owWVul7E6dzW9nHsR5RbFjpwd9PHrg8ZOQ1ZRFt2DulNWXUZd\nXR2BvoFU1VRJECXO6aw1USePB2/cuJHg4GAGDBjAuHHjiIuLs86pqKjgkUce4ccffyQkJIQS2Z65\npGSnpmJnb9+ia/2Cg6nT6ag3GtG28B5CnMuYMWMYM2aMzdhDDz10xrkff/zxhViSuEDer3ifK5yu\nYGXhSqb7TefNvDc5WHSQm8NvZs6mOYwJHsNVUVeRlJLEFVFXEB0YTWFZIdEh0dToaySIEud01kxU\nU44Hf/XVV9x8883WOgNfX9/zt1rR4eRlZqJtwVYegLObG1onJ/Kzstp2UUKIS56uQceC8gUM0w7D\nxc6F3cW7uSvkLlZnr8ZP7ceAwAHsPL6TwWGDOVJwBEwQGxxLfkk+Pm4+ONg7oNHI2Stxdmf9DmnK\n8eC0tDTq6+sZPnw41dXVTJ8+nTvuuOOM95Mjwn8/BZmZ2Lu4tOhaB0dHtE5O5B49Slgz+0yJjqkl\nR4SFOB/eq3iPoU5D+a7oO6b4T+GZvc+wIHoB/Xz6sT1nO9dGXcubP73JTV1vIqZTDJmFmcQGx7Ip\nfRMeLh6ShRJNctYgqil1LvX19ezZs4dNmzah1+sZPHgwl112GTExMafNlSPCfz+F2dk4ubesIZ2j\nkxNqBwdyjh5t41WJ9tKSI8JCtLWahhoWlC/g/3z+jxl5M6jR1TC602jWHV/H5KjJzNo0ixuvuRE7\nlR2F5YX0j+hPal4qY/qO4bOSz3BzcpMgSjTJWbfzmnI8ODQ0lGuuuQYnJyd8fHwYOnQo+/btOz+r\nFR2KyWSivKgIF0/PFl1v7+iISq2W7TwhRJt6r+I9hjkNY3XxaqYFTGPx8cXcHnw7G/M2EuUchau9\nK1klWYzoPII9x/bQL7wfKbkpRAdGU1BagJPWSYIo0SRnDaKacjx4/Pjx/Prrr5jNZvR6PTt27CA+\nPv68Llp0DMU5Obh6eODUiu08xc6OAgmihBBtpKahhjfK32Cq+1TWlK2hp31PGpQGiqqKGBY4jC1Z\nWxgfPZ4t6VsY3nk4u4/tpm94X1JzU/Fx9cHD1YM6Y50EUaJJzhpENeV4cNeuXRk9ejQ9e/Zk0KBB\nPPDAAxJEXSLyMjLw9PfH0dm5Rdc7OjlhVhTyMjPbeGVCiEvVwoqFjHAewbqSddzZ6U6+yP6CKRFT\n+DrzayZHTWbV0VWMix7HlvQtXB5+OSkFKfi6+OLs4EyNroYg3yDpESWa7JxHD5pyPPipp57iqaee\natuViQ4vLzMTdx8fnFoYRDk4OlLf0EC+BFFCiDZQ3VDNm+VvsjpwNdcevJYNXTcw8shIZsXM4qXf\nXuLNfm9SqCvE1c4VNwc3Kmsq6eLfheNFx4kNjiWvJI9g32AJokSTScdy0WJ5GRm4ennh6OTUousd\nnJwwGo00mM1US9NNIUQrLaxYyEjnkSSWJTLWayybijZxY8CNJGQncF3odfyU9RPXR1/Pz5k/MyJ6\nBLuzdls7lccGx5JbnEuwX7A02hRNJkGUaLH8zEyc3NxavJ3n4OhIncFAYGSkZKOEEK1S3VDNW+Vv\nMdNrJm/nv81TgU+xKGsRUyKm8Hn659wZfSer0lYxPno8m9M3MzxqOEnHkugX3o/UvFRrEHVyO8/d\ntWWnjsWlRYIo0WJ5GRk4uLq2eDvP0cnJEkRFRMgJPSFEq7xV/hbXOF/Djood9HXpS4G+AA+tB664\nkqvLpY9nH5IKkxgeOpytGVstReVZu60n86zbeX6ynSeaToIo0WJ5mZnYOzu3OBNl7+BAvdFIQESE\nZKKEEC1Wai7lnfJ3+Lf3v3k973VmBs/kg6wPmBI+hS/Sv+C2zreRkJXAiLARpJWk4e/qj5eTF6mF\nqfQM7UlKbgpdgruQW5IrheWiWSSIEi2ir6lBV1UFdnYtrolSqVQ4OjnhExQkmSghRIvNL5vPLW63\nsL96P74aXyK1kWwt3cqk4El8kf5F41ZezImtvM7D+eP4H8QGxKJCRW5pLlH+UZKJEs0mQZRokYKs\nLAIjIjDU1v5lJurYwYOsf/99flm+HINOd8Y5Ti4ueAcESCZKCNEieaY8llQu4QXvF5ifN5+ZwTNZ\nkr2EycGTSSpJwtvBmxj3GH7K+onrOl/HlvQtjIgewc7MnQyKHMTR/KOE+4Wj1WitheUSRImmkiBK\ntEhuRgZBUVEYamtPq4mqr6tj4f33M2vUKNL37GHLp58yJTqafZs2nXYfJxcXPP38JBMlhGiRuaVz\nudfjXlL1qdSYaxjtOZoPsz5kSsQUPjv6GXdG38nmY5vp2aknXg5e/Jr1K8OihrEjYweDogZZ66EM\ndQaqdFX4evhKECWaTB5RLVokPzOToKgockpKcDhlO89sMrFg0iQA3ktJwcnVFYADW7bw+sSJPL18\nOT2GD7fOd3ZxwcXLi/zMTBRFadLzGoUQAiDdmM6K6hWkRKbwz5R/8nTQ03yf/z1d3brS2bkzPxz7\ngVf6vcKsX2YxPno8SblJhHuF4+viy47MHbxw3Qus+m0VscGx5JfmE+gTiJ2dnQRRoskkEyVaJC8j\ng6DISGr1eptM1MqXX0ZXWclTy5dbAyiAHsOH8/Ty5bw+aRKlubnWcUdnZ+zUajT29lSWll7QzyCE\nuLjNKp3FY16Pcbz2OIf0h7jN7zbeyXyHRyMf5YdjP3CZ32X4O/mz5ugaxkePZ+PRjYzoPILSmlKK\nq4uJDYglOTuZ+NB4a3sDQFociCaTIEq0SH5WFoGRkdSdUhOVvmcP6957jye++AKtvf1p1/QYPpyx\nDz/M/z34IIqiAJZMVK1OZ2lzIHVRQogmOlh3kA36DTzu9TjzcufxeODjHKg6QL4hn+v9r+fzo5be\nUDvyduDl6EWMdww/pf3ENTHXsDNzJ/0j+qO2U3M4+zDxofHWonJAMlGiySSIEi2Sn5lJYEQEtXo9\njk5OKIrC0ief5Lb//AfvoKC/vO7m556jKDOTPQkJgKUmSq/TWRpuSl2UEKKJXix9kZneM8kx5LC1\naisPBTzEuxnv8nDkwxQbivm9+HfGh4/nm9RvmBA7gZq6GpJykhgaNdRSDxU5iIaGBo7kHiEuNI7c\nklxrECUdy0VTSRAlmk1RFGsmyqDX4+jszO61a6ksLGTkvfee9VqtvT23v/IKnz/3HA0NDTi5uGDQ\n6yUTJYRosp21O9lt2M3DHg/zSu4rTA+cjt6kZ3Xhau4Pu59lGcu4MfxGnNROfJvyLTd3uZmtGVsZ\nEDoAF3sXdmTsYGDkQI4XH8fL1Qt3Z/fTtvMkiBJNIUGUaLaqsjLs7Oxw8/S0ZqKWz5nDbXPnotac\n+6zCoPHj0To6sv3bb3FydrZmovIkiBJCnIOiKDxT8gz/9vk3OXU5JJQnMC1gGh8e+5AJgRPw0nrx\ncdrH3Bl9J0mFSdir7enh14MNaRu4OuZqFEWxtDeIGmSthwKs23kmk4l6Uz1Oji3rfycuLRJEiWY7\nmYUCqKutJffQIWrKyxk4fnyTrlepVNw0cyar3nwTp1NqogpkO08IcQ5rdWspNhdzj/s9zMudxyOB\nj+Bk58QHWR/waOSj7CrZRa2plqsCrrJmoVQqlbUeKr0oHRcHFwI9A22CqFOfm+fq7ConhUWTSBAl\nmu1kPRRArV7PlqVLGf/EE6jV6ibfY+C4cVQWFVFfUWEJoiQTJYQ4B5Ni4pmSZ3jN9zVy6nL4oewH\nHgt4jO/yvyPaJZqeHj1ZkrqEe7tYygpO1kPlVOZQVFNEn6A+7Mi0bOUBp2eifKXRpmgeCaJEs+Vn\nZRFwIoiqr6khbedORtx1V7PuoVarue6xxyjat49avZ6A8HAKjh2zntoTQog/W1q5FH+1P2NdxjI/\ndz4P+j+It9abdzPf5bGox9DV61iZuZK7ou/iQPEBTA0m+vr35ae0nxgZMxI7Oztrk01oDKIURbHJ\nREkQJZpKgijRbPmZmQRFRqIoCo4GA0MnT8ahBQ8hHnbHHZQcPUpVSQnOrq44u7lRWlBwHlYshLjY\n1TTUMKt0Fgv8FpBnzGN56XKeCHqC3RW7ya7NZpz/OL499i2X+19OsEsw36R+Y93K25BqqYcCrI97\nURSF5Oxk4kLiKKsqw15rj5uLmwRRolkkiBLNdjITZaitxQPOeSLvr7h5exPYrRv5+/cDEBQZKXVR\nQogzeqP8DYY7D6efYz9ez3udezrdg5/WjzfS32B61HQ0dhqWpC7hvpj7APgmxRJENTQ0sOnoJq6O\nvpq6+joO5BygX0Q/cktzcXF0wdvNm+OFxwnzDwOgsroSDzeP9vyo4iIiQZRotoKsLIIiI9m7YQOo\n1UT16dPie8UNH07J4cMABERESF2UEOI0BaYC3il/h5d9X6bAWMBnxZ/xVNBTHNMfY0PRBu4Pu5+0\nyjRSKlO4Luw6kkuSqaqrYlDQIPbl78Pb2ZswrzD2Ht9LjH8MLg4uNvVQ2YXZhHYKBaCyphJPN8/2\n/LjiIiJBlGiWkz2iAiIi2PrFFxhdXFp1iiVqwABMtbUcP3SIoMhI6RUlhDjNS6UvcY/HPURoI3g1\n91Xu8LuDQPtA3s54m3vD7sVd687StKXc0fkOtHZavk39lpu63ISdyo4NaRu4JuYaALYd3caQ6CGA\nbVG5ZKJES8kDiEWzVBQX4+DkhFarZd9PP6Hx9W3V/Vzc3LDv1IltK1cSFBVF8s6dbbRSIcTfQXJd\nMt/VfEdqRCrZddl8Xvw5h3ofoqK+gk+yP2HfsH2YGkx8mvYpm8ZsAmDFkRW8d/V7AGxI3cCMHHqB\nqAAAIABJREFUK2YAliBqQr8JlvtmJ9MnypJFzy7KJtT/RCZKgijRDJKJEs2Sd6K9wR8bNhAQHY2z\nZ+vS3k4uLigeHmxbscKynZeR0UYrFUJc7BRF4YniJ3je+3m81F68nPMyD/g/QIB9AB8d+4gxncYQ\n6hTK+pz1RLhFEOcZx8Hig1TUVTAkZAhVhip25exiRPQIFEX5y0xUdlG2NRNVUV0hQZRoMslEiWYp\nONFoc9s339Dlyisp2bu3VfdzcnHBqFZjqKhAZTLJdp4Qwmqtbi3HTMeY5jmNTEMm35R+Q0qfFIwN\nRt7JeIdVA1cBsDh1Mfd1sRSULzu8jEldJ2GnsmPj0Y1cHn45LvYuHC08ilatJcwnzHoy76+282LC\nY9rnA4uLjmSiRLPkZWbiHxLC7v/9j7C+fXF2dW3V/ZxdXKjV6xlyyy0c+fVXSvLyMNXXt9FqhRAX\nK6Ni5PHix3nL7y20Ki1zsufwSMAj+Gh9WJG7ghjXGPp69uV4zXF+LfyVSZGTUBSFr498zeS4yQCs\nPbyWsV3HAo31UCqVivzyfDRqDX4efsCfCstlO080gwRRolkKsrJQ6uoI694dOweHVgdRjs7O1Op0\nDLn1VrZ/+y1e/v4UZme30WqFEBert8vfJtY+ltEuo0mpTeF/5f/j8aDHURSFN9Lf4KnOTwHwUcpH\n3Nb5Nly0LuzM34lGpaGPfx8URWFdyjrGxtoGUQD7s/bTM6InAGazmfzSfIL9ggEJokTznDOISkhI\noGvXrsTExDB//vy/nLdr1y40Gg3fffddmy5QdCx5mZkUpaYy5JZb0NfUtDqIOvnsvJgBAzDq9fgH\nBMiWnhCXuAJTAfPL5vOm35sAzMqexRNBT+Cp8WRj8UaMipHRnUZjNBtZnLqYKbFTAMtW3uT4yahU\nKvbm7cXdwZ1o32jgDEFUuCWIyi/Nx9fTF3utPWBpceDhKkGUaJqzBlFms5lp06aRkJBAcnIyy5Yt\n4/CJnj5/njdz5kxGjx4tj+34m8vPzCQjKYnLbrwRXU0NLm20nadSqRhw/fVoFEWKy4W4xL1Q8gL3\neNxDF/suHNAdYEvlFh4NfBSAV9Je4dnoZ7FT2fHD8R+I9Ygl3isec4OZFUdWWLfy1h1ZZ93KK6sp\n43jpcXqF9gJsM1Gn1kOBJRPl6S59okTTnDWI2rlzJ9HR0URERKDVapk0aRKrVq06bd67777LhAkT\n8PPzO28LFe2voaGBkqwsfEND8Q0JaZNMlL2DA6b6esxmMwPHjUNXVCQNN4W4hO027Gadbh3/8v4X\nAP86/i+eCX4GV7Urv5X9xrHaY0wOtgRK7x9+n6ldpwLwc87PBLgGEOsdC8DaI2u5tuu1AGzP2M7A\nqIFo1JazVKcGUdlFjfVQINt5onnOGkTl5uYSGtr4zRUSEkJubu5pc1atWsXUqZZv5NY0XhQdW2lB\nAU5aLQOuuw7grEGUoiiUHDtG6i+/kLlrF3V6/RnnqVQqnE7URXUfNoya4mKyz5DtFEL8/TUoDUwv\nms5c37l4qD34peoX9un38XDAw4AlC/VM9DNo7DQcrjjMkcoj3Bh+I3BiK+9EFqpEV0JyUTJXRl4J\nwLa0xq08Y72RtPy0xpN5BbaZqIrqCtnOE0121hYHTQmIZsyYwauvvopKpUJRlLNu582aNcv652HD\nhjFs2LAmL1S0v/zMTOyBfmMtKXJ9TQ2dgoJs5tTpdGx8+21+XryYeoMBv8hIjHo9BampxI0Ywain\nnyZ26FCba5xObOm5ursTPXAgx/btu1AfSbSxxMREEhMT23sZ4iL1WdVnGBUjd7vfjaIoPJ31NHPD\n5uJo58gflX+wt3Iv3/T/BoAPjnzAfV3uw15tj9Fs5LvU79hz1x4AElISGNF5BA4aB8BSD/X8tc8D\ncCT3CJH+kTjaOwKWTFRUUBRg+eWvqqYKd1f3C/3RxUXqrEFUcHAw2aeclMrOziYkJMRmTlJSEpMm\nTQKgpKSE9evXo9VqGTdu3Gn3OzWIEhefo3v3ophMxF52GWAJopxcXKxfP5KYyOI77iDmiit4+Jtv\nCOvTxxqI1+l07Pz6a5bccQddrrqK2997D8cTWayTxeUAl91wAwdnzrzAn0y0lT//cjR79uz2W4y4\nqJSaS3m25FnWBq9FrVLzTck31Cl1/NP3nwDMS5vHE1FP4Kh2RFev44v0L9g73tKnbm36Wrr7difM\n3ZJRWntkrfVUnqHeQNKxJAZ3HgzYFpWDpSZqWJ9hAOj0OhzsHdBqtRfqY4uL3Fm38/r3709aWhpZ\nWVkYjUaWL19+WnCUkZFBZmYmmZmZTJgwgffff/+MAZS4+O3ftIlOnTuj1lhib71OZy0s3/jOO3w4\neTL3LFnCQ8uWEd63r00m08HFhSvvu4//JCej1mp5ZfBgSrKyANsg6qp//hOV0UhFScmF/XBCiHb1\nXMlzTHCbQD/HftQ31PPc8ed4Pfx17FR2pNSksKVkCw9FPATAl+lfMqTTEMJcLUHTJwc/4a7udwFg\nNBlJSE3g+vjrAdiRsYNuQd1wd7Jkl06th4I/PfKlRuqhRPOcNYjSaDQsXLiQUaNGER8fz8SJE4mL\ni2PRokUsWrToQq1RdBDH/viD6EGDrK9P1kSte/VVNr37Ls///jvdrrnmrPdwcHHh7sWLueLee3l9\n+HBKsrJwcnZGfyKI8uzUCbWTE9tWrjyvn0UI0XFsr93O/2r+x1yfuQB8WPghUY5RjPQcCcD8tPlM\ni5yGq8YVRVH4b/J/mdHN8jy8Il0RW7O3MiHW8ky8xIxE4jrFEeAWYHmdksiw2GHW9/pzEHWs4Jjt\nw4elHko0wzkf+zJmzBjGjBljM/bQQw+dce7HH3/cNqsSHY6pvp7ynBz6jh5tHdPX1JC1ZQtHfviB\nmVu34vmn+qi/olKpuObxx1FrNLwxciTOQUHWTBSAV2gou9ev59oThxWEEH9fJsXE1KKpLPBbgKfa\nkypTFf/J+Q8J8QkAZOgyWFWwirR/pAGwIXcDWpWW4YHDAfjq8FeMix6Hm70bAD8c+oEb4m+w3j/x\nSCLPjHnG+vrUIKpKV0VtXS2dvDoB0t5ANJ90LBdNcnjbNhQ7O2L797eOGfPz2b10KTPWrWtyAHWq\nfzz6KH1vvhnt4cNUlZVZx8N79ybt99/bZN1CiI5tYcVCfNW+THaznKx7Pe91RnmOordLbwDmps7l\nkchH8Lb3BuC/hyxZqJPlAp8e/JS7u98NWNqw/HDoB27oZgmiDPUGdmXt4oqYKwAorixGb9QT6mvZ\nvsvMyyQyMNJ6L3n4sGguCaJEk+xcswaDyYR/mCXtXZ6bi0tmJje99Rb+MS1/WOfNr7yCxtGR3UuW\nWMe6DBhAnU5HgTTdFOJvLbc+l7mlc/m/Tv+HSqUiy5DFewXvMTfMsq2XVpPG6sLVPB71OACHKw6z\nt2wvk6MsAde+on2UGcoYFjYMgF05u/B08qSLXxcAfk//ne7B3XFztGSpDhw7QM/wntagKTM/k8ig\nSOt6ZDtPNJcEUaJJdq1Zg2tAABqtlgazmSV33km5kxPxI0e26r52ajX+o0eTt2sXBxIs6fvg6Gic\nvL3Zs359WyxdCNEBKYrC1KKpPOL5CLH2lgaZzxx7hhmBMwh1sGSK5qTOYXrkdLzsvQB4+9DbTOk6\nBUeNpT3Bpwc/5c5ud2KnsvxT9sOhH7ix243W9/hzPdSe9D30juptfZ2Zn2ltbwDSaFM0nwRR4pyK\njh2jorCQ0G7dAPjxjTcw19eTDa1+7AuAu58fETfdxCf33UdVURGBkZEYgT0ngiohxN/P19Vfk1Gf\nwQs+LwCQWJnIzpqdPBVkebDw4erDJBQlMD1qOgClhlKWZy63diivN9fzZfKX3NntTus9T93Kg9OD\nqKT0JPp17md9fXI77yQJokRzSRAlzmnP+vUExMYSEh1NYVoaCa+9xr2ffkqtXm/TJ6qlXN3dsfP1\nZdA//8nXM2YQFBlJSVkZB7duxWgwtMEnEEJ0JMWmYh4vfpyl/kuxV9ljVsxMz5zOgvAFOKmdAEsW\n6onOT+CutbQm+DDlQ24IvwF/J38A1masJcYrhhhvSznBkaIjVNdV0y/YEiQZ6g3sztrNkJgh1vf9\ncxCVkZdhs51XUV2Bp5sUloumkyBKnNPudetw8vYmKCqKz6dMYexzz+EWEIDW3h61Wt3q+7u6u1NT\nVcX42bNJ376drN9/x97JieDYWJJ/+aUNPoG4lCUkJNC1a1diYmKYP3/+aV//8ssv6dWrFz179mTI\nkCHs37+/HVZ5aZlePJ3b3W9noNNAABYXLsZL48XNPjcDcLDqIJtLNvNopOWhwwaTgXeT3+Xxbo9b\n77Hoj0U82OtB6+vvD33P+G7jsbOz/LP2e/rv9AjuYa2HqtRVkleWR9eQrtZrMvNtM1FllWV4e3if\np08t/o4kiBJnZTQYOJiYSF1DA3XZ2ejKyxk5fXqbPHz4JBc3N3TV1Tg4O3P7e+/x+dSpBIaHE9Gn\nj2zpiVYxm81MmzaNhIQEkpOTWbZsGYf/9GzGqKgofv75Z/bv38+LL77Igw8++Bd3E21hTc0adhp2\nMsdnDgDlpnJeyn6JtyPfthZ8/+vIv3i689O4aiw/Yz49+il9ffvS09vSmiCrMotdBbu4JfYW632X\n71vOrT1vtb7edHgTw7sOt77em7GXnhE9rQ8hVhSFrPws2yCqogxvTwmiRNNJECXOKvmXXwjv0YOC\nrCz++Oor7vrwQ9QajSWIaoOtPGjMRAH0GDOG8H79cDUY8AoLk+Jy0So7d+4kOjqaiIgItFotkyZN\nYtWqVTZzBg8ejIeHpQ5m0KBB5OTktMdSLwkV5gqmFk1lsf9inO2cAXjx+Ivc6H0jvVx6AfBr6a/s\nrdzLtMhpAJgaTLx24DWe7fms9T4f7fuI2+Nvx0lr2fo7UnSEopoiroi4wjrnx0M/Mqr7KOvrpPQk\n+kc3tmgpKi/C2dEZNxc365hkokRznbPZpri0Ja1bR9/Ro0mbPZvukycTcaJPVE1VFS7uZ35IZ0ND\nA+WZmZQePUpdTQ0qlQr34GD8YmNx8jy93sDF3R3diSAK4NYFC3i+a1eqysqoKimh6NgxOoWHn58P\nKP7WcnNzCQ0Ntb4OCQlhx44dfzl/yZIljD3xgG3R9p4ofoLrXK5jmPMwAHZW7+Tb0m9J7pMMWLJD\nTyc/zdyuc3FUW07gfZP1DUHOQVzhbwmQ6s31LD2wlM2TNlvvu3y/JQultrOUF5TWlJJSkGJ9Xh5Y\ngqhRfRqDqoy8DJssFEgQJZpPgihxVrvXrWPCs8/i0dDALafUk1RXVuLm0XiKpaGhgfTNm9n98cek\nbdiA1skJn5gYnDw9aTCbqczJoTglBb/YWLrfeCP97r4bj+BgoHE77ySfsDCiR47kyA8/0GfUKPas\nX8/oKVMu3IcWfxunPr/xXLZs2cLSpUvZtm3bGb9+6gPU//ygZXFu31d/z9barewL3wdYOpVPyZjC\ngogFeGksLQy+y/+OWnMtt4XcBliCqlf3v8rL/V623mf10dXEeMUQ5xNnnbN833KWTGjsNbcxeSND\nuwzFXmNvHUs6msTzE563vv7zyTw4EUTJdt4lKzExkcTExGZdI0GU+Ev5R49SW13Nzs8+QxsaatOV\n/GQQpSgKB7/9lh9ffBGNvT0DH3yQ0a+8gtcZMkcmo5Hjv//OH199xVs9etD9ppv4x7//bbOdd9I/\npk/n/8aMISw2lj0JCRJEiRYJDg4mOzvb+jo7O5uQkJDT5u3fv58HHniAhIQEvLy8znivU4Mo0TwF\npgKmFk3l26BvcbWz1DktzF+Il8aLf/r+E8Dy0OHDz7Gwx0Jr36f1OetRFIWxIY3ZwUX7FvFQ78ZH\njx0sOIjOqOOysMusYxsObWBUt8asU5W+ityy3NOKyqOCG3tEAZRXlksm6hL251+OZs+efc5rJIgS\nfylp/Xq69utH9rZthP5pi6Omqgo3lYoPrrwSo07H+HfeIXrkyLP+5q+xtydq6FCihg5l9Lx5/Pz6\n67zTpw/9p06lprLSZm7n3r0p12g4sm4dhw4dot5oRGtv/xd3FuLM+vfvT1paGllZWQQFBbF8+XKW\nLVtmM+f48ePcdNNNfPHFF0RHR7fTSv++FEXhgcIHuM/jPoY4WdoN5NTlMDdnLtt6bLP+zPjo2EeE\nO4dzTadrrNfN2z+PZ3s+a52TXp7O3sK9rL5ptfX+J7fyTs5RFIUfD/3IzDEzrXP+XFQOkJ6bzuDu\njdt99fX16A163F3PXKYgxJlIYbn4S7vXrqU6I4OAwYMJ7dr4G5yiKGStWYN640Z6TpzIo0lJxFx9\ndbO2Tpy9vBj9yis8uns32du20aOkhLKsLOvXPXx8qHV0RFdWhl9gIId//bUtP5q4RGg0GhYuXMio\nUaOIj49n4sSJxMXFsWjRIhYtWgTAnDlzKC8vZ+rUqfTp04eBAwe286r/XhZXLSbXlMtLPi9Zx6Zn\nTmda4DRinSydyqtN1cxJncNr8a9Z52zO30xRbRG3RDaewFu4dyH39bzP2rH85FbexF4TrXMO5x9G\nY6chxr/xcVS7j+626Q8FkJadRpfQLtbXJ3tENefnmBCSiRJnVKfXk/rzz4QHB1Pr4EDIid/QTXV1\nfPfQQxT//DNet9/OkEcfbdX7eEdG8uCmTVyv0fB/l13GxM8+o8s1lt9EQ2Nj6X/99fz87rskrV9P\nzxEjWv25xKVnzJgxjBkzxmbsoYcat4MWL17M4sWLL/SyLgnpxnSeK36On0N/xl5lySSvKlvFAf0B\nvuzypXXef1L+wyi/UfTx6ANYgqN/7/k3L/V5CY2d5Z+pqroqPj34Kfvu3me9Lik3CQXF2mAT4MeD\nllN5pwZD21O2c+NljY+DAUjNTrUJoqS9gWgJyUSJM9q3aRPuWi03zJnD8dRUwrt2paaoiA+HD6eu\npgb3SZPwioo6942awM7OjiIPD25YsoQVd9/Njo8+AiA0Jgb7gACcPTzYtXx5m7yXEOLCMCpGJhdM\n5kWfF4l3iAegrL6MhzMeZkn0EhztLNmklJoUlmYvZX5848GVDbkbKDeWMzGyMcP0ycFPGBk+klD3\nxtOWnyZ9yh1977AJmH489CPXdLvG+lpRFLYf2c7g2Matu4rqCvR1egJ8AqxjcjJPtIQEUeKMNr3/\nPo7OzvSdMIHc9HTcXVz44KqriBo+nNtWrEBXW2tzOq+1XNzc8OnRgyk//0ziq6+yae5cQmJiyDl6\nlIlvvIEhL4/CU7b7hBAd27PFzxKoDuQxz8esYzOyZjDBZwJXul8JWAKcGQdn8Fz0cwQ4BljH/r33\n38zqM8vasqBBaeCdpHeY0X+G9V5Gk5Gv933NnX0bn51Xbajmt6O/cXX81dax7JJsTGYTkf6NJ/HS\nctKICYmxCb4kiBItIUGUOE1DQwNHN25kzLPPUnT8OH7e3nw6Zgz9776b0S+/jJ2dHTVVVbi2YRB1\n8oSeb3Q0U3/9lQMrV2LYvZvjKSn0HDsWF29vfmjCSQkhRPtbXbOab2u+5eOAj62BypqyNWyr2sYr\nYa9Y560tXEumPpNHoxrLAtblrKPWVMuEiAmN89LX4uXoxeCgxmzS2iNr6ebfjUjvxuDop0M/cVnU\nZbg7NRaHbz+yncvjLrcJmFKP227lgQRRomUkiBKn2fDOO6BScfWjj5KybRsR5eUMeewxhs1sPO3y\n5z5RZ2IyGKirqsJYXU2D2XzWuae2OXAPDOSBTZuoTE6mcpvl9M7QqVPZs2IFpvr61n9AIcR5c7z+\nOA8UPsCywGV4qy1BSbmpnKkZU1kavRQXteVJB3XmOmYcnME73d/B3s5SL3WyFmp2n9nWNgcAbye9\nzfR+020CoU+SPuGufnfZvPfqP1Yzrvc4m7Hfjvxms5UHp9dDgdREiZaRwnJho6GhgYRXX6XL1VdT\nW17O1meewb1fP4Y89pjNvOrKSlxPdCxXFIWSgwfJ+uknCvfsoeiPP6jKzsZUW4vawQEUBbPRiHOn\nTnhFRxPQvz+BgwYRcfXVOJ7oYO7i5mbTK8rF15d7f/yRudHRbJ43jzGPP87/Xn6ZxA8+YGQri9mF\nEOdHvVLP5PzJPOH1BJc7XW4dn5E5gxu9b+Qqj6usY2+kv0E3927WlgYAKzJXoELFDeE3WMeSCpI4\nUnqEW7s2PhevqKaIrRlb+WLSF9Yxc4OZdQfWMXu8bcZ6e8p23rjnDZuxtJw0xg62bdsimSjREhJE\nCRu7V6ygVq/nqnvu4eOxYyEggPjJk0+bV11Ziaq8nC1PPUXKypWo1GoiR48m/B//YMCTT+IREYGD\nZ+NxYbPRiK6ggLKUFAp27+bgJ5+QcN99BA4cSLc778TV1fW0XlGdoqLI9fNj+/vv4x4YSKdevVg9\nZw5XPfAAWkfHC/L3IYRoumdLnsXdzp2nvZ62jq0oWcH26u3s6bXHOpZWk8abGW+SNDTJOlZnruO5\npOdYcsUSm4zTqzte5ckBT2KvbuwT99UfX3F93PW4OTQ+9+739N8J9Agk3Lex0W9tXS2Hjh+yeWYe\nWDJR02+ZbjNWWlFKbGRsKz69uBRJECWszCYT3//731TX13Ng0SICevTg8ImTeScpikLWTz8Rn5rK\nb/ffT6/77mPCunX4xMeftb+K2t4e97Aw3MPCiLjaUvRp1OnITEjgwJIlBG3ezLGaGnTDh+PSqZP1\nuqD4eC674w7Wz5xJtxtuYH9pKT9/9BH/kGyUEB3KV1Vf8UPND+wK22Xdijted5xpGdNYG7cWV7Wl\nU7miKEzZP4UXYl4g3Lkx4Hnv8HvEe8YzPHC4dSylLIWt2Vv5eMzH1jFFUVi6aylvXf+WzfufaSsv\nKT2J+NB4nBycbK5PPZ5KTGiMzdyi0iKu7H9lK/8WxKVGaqKE1e9ffolKrSYqMBAVcOMHH3AsJYWw\nE0HU8cREvhwyhE3Tp3NUreb2gwe56tVX8e3WrUUN6uxdXIi9+WYmrFuH+p57MFRWsqRrVxKfeQZD\neTkAEfHxlFZXc9vKlWR98w011dWsnTePOr2+LT+6EKIV/jD8wfTi6Xwf9L21DsqsmLkj7Q6eCHqC\nAW4DrHM/y/mMyvpKHo1s/EWovK6cefvn8Vr/12zu+9qO13ikzyO42rtax7Yf247BZGB4VGOwpSgK\nq/et5vpe19tc/0vyLwyJG2Izll+Sj6O9I97utlt3RWVFdPLuhBDNIUGUAMBUX8+aOXPQqNWoq6v5\n59dfo6uupq62Fvv6elbdeivr7r6bvo8+yt3793O4rg53H582e3+PyEjqr7ySe/bvp66igiVxcexf\nsoSIuDgyDx0iauhQbli4EI+qKvw7d2bLe++12XsLIVquxFzCjXk3srDTQno69LSOz8+djx12PB3c\nuLVXXFfMM8nP8FHvj6xNNAFe2fcKN4TfQLxXvHUsuyqb79O+59G+tlnn939/nymDpmBn1/jP16G8\nQ+iNegZEDrCZu+XAFob3GG4zdjDzIN2iup32OYpKi+jkI0GUaB4JogQA2z/7DA8fH/TJyUz88kuc\nvb05dvgwvTw9+axfP3zj47kvOZn4yZPR63Q4u7qi0bTdbrCntzcVZWW4hYQw6sMPuXntWvYvWULJ\n+++Ts2sXAL0nT8a3f3/0R4/y42uvYaiubrP3F0I0n0kxMTl/Mre43cJEt8bGmDuqd/B2/tt8HvM5\napXaOj794HTuDLnT2pkc4GjVUZamLWVWn1k2916wawH39LgHb6fGjFGJroQ1h9ecdipvxa4V3NL/\nFpuMuLHeyPYj2xnabajN3EOZh+ge2f20z1JcVoyft1/z/gLEJU9qogQmo5E1s2bhpNdjCg0l/ppr\nqMjMZOu99+JrMHD777/jdcqDWStKS/HwPv0Ui0mvpyo9Hd3x4xgrKjBWVmKuq8NOq8VOq8XB2xvn\noCCcg4NxDQtDdcpvku5eXlSd2MIDCOjXj9t+/ZXtb75J8TPPsPONN+g/YwbXzJ7NskmT8PHyYuM7\n73DdCy+c378cIcQZKYrCY0WPoUbNK76NvZ9K6ku4NfVWPoj6gBCHEOv4N3nfkFSRxOKrFtvcY/rv\n03mmxzMEOQdZx3Oqc/j80OccuveQzXt+svsTxsePx8fFx+YeK3at4LP7PrOZu+voLmKCYvBy9bIZ\nP5hxkIFxts9HNJlMVFRX4OPZdtl1cWmQIErw8+LFOOj1OEdHEzt6NAc/+4wtTz6JMS6OkHvusQmg\nACrLyvDy9qZw2zaKfvuNou3bKdm1C0NJCW6RkbiEheHg7Y29pyd29vY01NfTYDRSV1qKPj8ffU4O\ndeXleMbH49OnDwFXXYWzVktlWZnN+6js7Lj8qad469VXCV2xgvQ1axj9ySfkms14A5vnzWPEtGk4\nt2HTTyFE07xV8Ra/1v7Kr6G/olFZ/ikxK2ZuT7udiT4TudGn8Vl1BYYCph2YxqqBq3DWOFvH12Sv\nIb06ne//8b3Nvef+Npf7e95PoGugdayhoYFFOxbx+aTPbeYezD2Iod5w2lZe4oFEhnUfdtq6D2Yc\n5J6x99iMlVaU4uXuhVqtPm2+EGfTpCAqISGBGTNmYDabuf/++5l5StNFgC+//JLXXnsNRVFwc3Pj\n/fffp2fPnn9xN9GR1NfVkTBzJmE9e3IgN5eoAwf4fflyJm3ezNzHH2dIr17WuSa9nuOrV3N40SKu\n3r+f7dOmETB0KBG33MKABQtwDQ/Hrok/hIyVlZQfPEjJ7t1krVxJ7ubN9KurY89LLxExYQJe3btb\nU/NBvXvT5fHHUfbu5avLL6dn797EjRlD1ezZfDN9Ond+8sn5+KsRQvyF76u/543yN9geuh13dWN3\n8Lk5c6ltqOWV8MbMlKIoPLDvAe4Pu59BXoOs47WmWmbsmMGiyxfZtC/IqMhgZcpKUh9ItXnPdSnr\ncHd0Z1DoIJvxM23lAWw5uIXp19m2MWhoaCA5K5lukbY1UVIPJVpMOQeTyaR07txZycyUQfWkAAAg\nAElEQVTMVIxGo9KrVy8lOTnZZs5vv/2mVFRUKIqiKOvXr1cGDRp02n2a8FaiHXx2xx3Kcy4uyu6v\nv1Zm29srqyZOVOqqqpSGhgZlrK+vUpiTo+T//LPy8733Kp97eioJo0Ypy++7T3lm/Pg2XUd2erpy\ns5+fsuOJJ5Svw8KUb7t1Uw7+97+KobRUeeuxx5SvFixQFEVRsjZtUt709FRe7tJF2fnJJ8rTKpWS\nvXdvm65FtJ2/y//3f5fP0RZ26Hcovkd9ld21u23G15etV4J2BSn5dfk240uOLVF6b+mt1JnrbMZf\n2vOSMmHThNPuf+f/7lRe+vWl08aHfTBM+XLPlzZjDQ0NSpfnuig7M3bajBuMBsX1VlelvLrcZjwz\nL1MJHh982r03/rZRGX7H8NM/rLikNeX/+3MWlu/cuZPo6GgiIiLQarVMmjSJVatW2cwZPHgwHie2\nVAYNGkROTk7bR3uizWX+8gsHvvySEU88wZZ778VnyBCuX7YMezc3SrKzCTUY+O3aa9n24IN4du3K\nTYcOMSohgbpevXAJCTn3GzSDp58f6Xo9A994g1uzsrj8vfco3rmTlVFR+O3aRfavvwIQPmIEt//+\nO4b0dLJWrsS/Xz+WjB6NUVoeCHHepRnTuCHvBpb4L6GfY7/G8do07jp6F8tilhFgH2AdT61JZWby\nTD7r+5n10S4AKZUpLExeyBsDbTuJHy49zPrM9Tze/3Gb8T25ezhaepRbet5iM74rcxcNSgP9I2yb\nae5I3UFscCyerp4244cyD52WhQJLJkqKykVLnHM7Lzc3l9DQUOvrkJAQduzY8ZfzlyxZwtixY//y\n66JjqC4o4ONx4/AND+fIokUUBgRwz5w5mPR6jrz3Hnvnz6erVkv/efMIHjXKpgi8sqwMTx/bwk5j\nSQm1x45hyM/HVFVl+e/k6Tk7O1RqNWoXF+x9fLD38cGhUyecIyNRO1ma4Lm4umKsq8NoNGJvb0/A\n0KEEDB2KoaSErS+8QPXSpWwcP56ezz1Hp8suw370aGp0Otx1OgrKylh+553cvnJli/pVCSHOLbc+\nl2tyrmG272zGuTY2tSw3lXP9kev5T+h/GOrReBLOYDZw6+5bmdN1Dj3ce1jHG5QG7vvlPl7q8xJh\nrmE27/Hs1md5euDT/D97Zx0e1fH18e9u3N1dIZAQXBM8WHGCtECBojWkRUopVqCFHy0aCsVCcQnF\nQwgJCYQQ4u7ubptsNmv3vH8gZdlNoC28LXQ/PPfZMOfM3Jm5O7Pnnjl3ro6KZJzjzw9+xtIBS6Gk\noCSRfuLRCcwdMFdq3AfEBWBk95FSbUjJS4Grvewn8+TLeXL+Cq80ov7Mj1JISAiOHz+O8PBwmfJN\nmzY9/3vw4MEYPHjwa5ct580hEgjw2+TJYHG50NTUxNBTp7B99mwwMTHwmzoVJp6eUJgzBxCJYDl6\ntEReYUMDRPHxMCVC7MyZ4CQloSU3F2w1Najb2EDV3ByKOjpQ1NaGoqYmwGIBYjFILIaIy4WgpgaC\n2lrwKyrAKyqCsqEhNBwdoe3ujn6qqqiIjIRV//5gPY2tUjU0xID//Q+7Tp/GLi8vhH74IbSdnNBj\nwAAkxMfDa+JENH77LfKDgxG+dy88li//J7pUzlNCQ0MRGhr6T1dDzhumRlwDr1IvfKr7KRbqLHye\nLmSEmJY5DaN1R2OR6SKJPF+nfg1nTWcssVkikX4g/QAA4HOXzyXSgwqCkFKTgovjL0qkFzcU43bm\nbRyYeEAivVXYigtRFxC/MV6qvv6x/vBZ5COVnpCdgFF9RkmlV9XJY6Lk/DVeaURZWFiguLj4+f+L\ni4thKWMpJykpCQsXLkRAQAD09PSk5ICkESXnn+Pal1+iJTUVWpqamB0VhVvLlsGDy0V5cDBG+PvD\noFs3bJ0zB+4DB0LE5aLm3j3UhoaiJjQUzZmZ0NfQgHq3bjCaOBEOX38NTWfnJwbTn4TEYvCKi9Gc\nnQ1OYiJcWCykzpyJFA4HhoMGwXD4cBh5eUGzQwfomJlBfehQeC9ejKxjxxC3eTOopgbW33+PkUeO\n4OasWQjdsAFm7u5wGDLk1SeX81Z4+eZo8+bNbSvLeSdoYpowumQ0JmhMwGr91RKyFQUroMRSwk7b\nnRLpfmV+CKgKQNygOIkb8fymfGyO34zwseHPXw0DACJGhBX3VmDn4J1QUVSRKOunBz9hXs950FWT\nXJq7nnAd3W26w9pA0ptVVleGouoi9O3QV6otsZmxWDdHeluUyppK9OjcQypdjpxX8qqgKaFQSPb2\n9pSfn098Pl9mYHlhYSE5ODhQRETE3wrQkvP2Cdu9m3aoqdEmJSVK8vWlq9270z4NDQrft++5Tmtl\nJW2wsqK7np50S0uLwocMocytW6kmLIxEra00f9QoCr11643X7UMPD4oMDaXWykoqOXeO4ufPp0Br\nawq0tqZfnZzo1nffESMWExGRoKmJfnZ0JF8tLXq8fDn99uGH9D81NdqmqUm1+flvvG5y/hrvy7h/\nX9rxZ+GIOeRZ5EmLKxYTwzASsp9KfqJOcZ2oQdggkZ7RlEFGt40ouj5aIl3MiGnY7WG0PXG71HkO\nxR+iQWcHSZ2jrLGM9DbqUTmnXCrP6N2j6XTEaan0Y3eP0bT/TZNKb2xuJPWh6iQUCqVkYxaOoWtB\n16TS5fy3eZ1x/0pPlKKiInx8fDBy5EiIxWLMnz8fLi4u+PXXXwEAixcvxvfff4/6+np8+umnAAAl\nJSVERUW9PctPzl8i5dIlPFy5ElqOjjASi5G2fj3sly3DlZ9/xuK5c1Fy9iyKT55E/ePH0GpuhuOW\nLTAfPx7KL3kWG2proWtgADGPB0FREQSVlRBWVkJYVQVRXR2YlhYwPB6Y1laQUAiWktLzg62mBkV9\nfSgZGEDRwABKJiZQtbWFopERDIyNUVtVBZVBg2AxYwYsZswAEaEpNRX1K1ag5eBBBB47BvOpU2E9\nbx5c16xBwrVrcGxuhmJwMFpYLOhpaMC3Vy98kZcHFS2tNnpCjhw5r+KZB6qTSif8YvyLhEfpZNVJ\n7KvYh4euD6Gj+Ef8UqOwEROiJmCbyzb01JUM9t6buhdcIRdfu34tkV7Hq8PG8I247X1bKnxkR+gO\nzOkxB6ZaphLpJXUleJz3GH6f+knV+3bsbXzQ8wOp9PiseHRx6CLzTQullaWwMLFopzfkyJEN66m1\n9fZPxGLh/+lUcmSQffMmfh8/HtZdu6IpIQGdly5Fry1b8PvSpWBFR0O7tBR6ffvCas4c1JmZYdeK\nFfCNiwMAiBobwU1IADc+Hry0NEScOgUnXV0w9fVQsbKCkonJk8PYGEoGBmCrqT0/WEpKIJEIJBA8\n2XSzpQWiujqIamshrK2FsKIC/IICMHw+GlVUwLawgPO4cdDo0gXq7u5Qc3ICS0EB0UFBOL55M3Ye\nO4bSs2dRdOIEFLS1EZqdjRWpqRDV1MB/4kSIRSK0EIFRUcHi9HS5IfUP876M+/elHa8LR8zBqNJR\ncFdxxwHjAxJLb/71/vgk5xOEdA6Bi7rL83QxiTEuchzs1e3h00UyHim+Nh4j7oxA1Lgo2GnZScgW\nBiyEioIKfLwk85RzytF5V2ekfpUKM20zCdn6K+vR0NKA/TP3S6QLhAKYzDFB+oF0mOpJGl67L+xG\nTkkODnwtGVsFAMb9jJF0PQmmRqZSMjn/XV5n3Mt3LP8PkHb+PAJmzYKJri54VVXQX7gQlq6ueOTp\nCUpLg+1nn6HrmjVQNTMDMQyiv/sOQ9TUkDljBrixsRCUlUG9Sxdodu8OdXd33PjtNxwKCoJux47P\nA8D/LiIOBydWr4ZybS06Kiuj5uJFtKxbB0F5OTS6d4dOjx5gx8RAUVcXHTZtgvOGDagJCUHpvHkI\nc3WF5dSp8Dp1CkcmToS9khKaampw1MUFc2NjoWFi8kbqKEfOf4EGcQNGl45Gd5Xu8DH2kfAORTRF\nYG72XFx3uS5hQAHAt+nfopVpxW7X3RLpXCEXH4V+hD199kgZUGHFYbiddxup8yVf7wIA/7v/P8zp\nMUfKgOIL+Tjy4AhCV4dK5QlOCkYnq05SBhQAxGbEYmiPoVLpfAEfDU0N8sByOX8JuRH1HkNEiNy+\nHXEbNkBXQQEdFixArI8PzPz8UFFeDp3583Fh3z5M/PJL1F++jMbgYHDCwqApEEC7SxfojR4Nqw0b\noObsDNZTF3gzh4OsNWug6+ICcU0NRGVlYOrrwXA4EgeJRADDgBgGYBgAAFtdHSx19T8+dXWhYGQE\nRWNjKBgbQ93dHZmJifh448bnbRBxOGiOikJTeDg8WCzE29tDzc4OuiNGQHfkSDj99BNu7d6NTp07\nI/WTT9DDyAjl6upwdHJC4bVrOOnigukREdDv0OEfuQZy5LxLlInKMLJkJLzUvfCz0c8SBlRUUxQm\nZkzESaeT6KslGbTtW+SLS2WXEOUZBSW25DYEyyKXoadhT8x0mCmRzhfxsejOIuwdtldqS4O82jyc\nijuFlK9SpOp4OfYy3Czd0NGso7Qs4jK8+3vLbFtMRgxWfbRKKr28qhymhqZgs1+5baIcOVLIjaj3\nFEYkws1Jk1B1+zaU1dTQeeRIlO3ZAwdPT3gePAhxaSnuf/01ZjQ0IKV/f+iOGQODqVNht38/5o8c\niQ379sHA2hqC7GxwzpyBMDsbgqwscLOycJDPR7aqKtja2lA0M4OCgQHY2tp/HFpaYCkrAyzWk/2l\nlJQAIogbG0Hl5WBaWkBcLsSNjRBXVUFcXQ1xVRW6A7BXVERJURGU7OyeHPb2UHNxgc66dUhNSYH+\n2LFwdXFBw507KN68GUhKQmc+H8yoUfC4fx+1kZFomDcPwspKOI4ejRx/f/i5u2O4nx/sx479py+L\nHDn/WjIFmRhVMgpLdJdgtd5qKQNqbPpY+Dr5YpSe5BYB/pX+WJu+FvcH3IehiqGE7EjmEYRXhiNq\nvHSM7A+Pf4CTnhMmO0+Wkq0NWIvlHsulYqEAwOeeD9aMXiOVLhKLcC3yGtZPWy8lq2moQXltucw9\nouTxUHL+DnIj6j2EU1CAax4eYCorocpmw0BFBS0KCig1NcUIKyuk9e0LZXt75GdmYuLZs7AaMwai\n3Fy0xsej9uef4Z2RAfaoUcgXCKDs7AwlJycoOztDc+JEVDQ04KSvL3zCwsBWVX1jdSYixN65g7Nr\n1+KHzz6DMD8fwrw88MLCIEhLg6i0FN56eqhNSoLwk0+g36cPzL/8EgQgb8YMFF69ioa9e6Hq5IQO\n06fj4d278FBXh6mGBhrFYoSOG4eSzz6Dp4+PfENOOXJeIro1GuNLx+MHwx8wT0fy5bxRTVEYlzEO\nxx2P4wM9yYDtqPoozImfgxu9b6CDpqS3N6o6Ct/GfouwMWHQUpKMTYwsi8ShhEOImxMnNR4jCiMQ\nXhAO36m+UvV8lPMIFY0VGOsufUN0P+U+7EzsYGNsI50v+RH6dOoj8wXDpVVyI0rOX0duRL1HEBFS\n9+9H3FdfQUNVFRCJYDF2LHS1tVFx/jx6u7hAs1s3WK1Zg8QTJ2BZVwf23r3InTULikZGUOnWDdVa\nWsju2BEfBAZC0dxcaoKrvHgRSra2b9SAAp4E8BnY2yO9qQmaMjxGDJeLDD8/xK9bB7fKSnB/+AGt\nsbFQtLBATwcHBBUXY46/PwQNDai7dg3dqqvBf/QInZcsQeKlS9AsK0PZoUO46O+P0cHB0La3f6P1\nlyPnXeVS0yV8VvUZjpscxzjNcRKyB40P4J3pjeOOxzFWX3JcZjZnYkLUBPh29UVffcnlvSpeFbzv\neeNw/8PoqCu57MYVcDH71mz4DPeBhZak8UJEWHlrJbaO3Ap1ZXWpuv7o/yNWj14NBba0MXQp/BKm\n9Jsis43hyeEY0GWATJncEyXn7yBfBH5PaMrPxw03N6QvXw4DZWUoMwzMNDWhXl2NkooKNHTtCptB\ngyA4fhwlvXpBcPgw7Lt2hf7KlbAvKoJdXh7ML19GlJUVtCdOhJKFhUyPTXVFBQxN384TLM+2OJAF\nW0MDzh99hDuNjVBdvx5WoaFwrK+H+YULMB4/HhZEKJ4+HbUzZkC1pAQ2X36JBA4H/MpKmDc3Q01Z\nGYY2NlAtKECAkxNiv/kGjEj0VtohR867AEMMNtVuwsrqlbhrcVfKgLpWdw3emd4463xWyoDKbs7G\n8EfD8aPLjxhrKinjiXiYEDQBcxznYJLtJKnzrgxdib7mfTG141Qp2W+xv4Ev4mN299lSsuSSZMQW\nxGLugLlSslZBKy49uoQPB34os63hyeEY4CbbiCooLYCNubT3So6c10FuRL3jiEUiRH/2Ge46OkIp\nNRXm+vrQEImgZ2cH+zlzoNHaCtOQELhra0PJzg4mhw9D7fFjHFVWhuv589AYMwYKL+wDlfDgAboO\nHNjm+WreohGlpaMDkVAIbnOzTLmikhI69emDlEePAAAsRUWouLtDd/FiqG3fjt+7dYNtZia0Z82C\nOpeLgcrKYC5ehKWHB5wmTgS/rAxGqqowVFVF5Y4duGFkhOLff38rbZEj598Ml+FiRvkMBHIDEWUd\nha6qXSXkxyuPY0nuEvh38sdw3eESslxuLoZFDMPGDhsx13quhIwhBrMfzIa9lj02d5ferd4v0w8B\n+QHYP3y/lKyGW4M1t9fg18m/yvQ0bfffjuVey6GqJO0FvxZ5Dd3tu8PayFpKxhfwEZ8Vjz6d+sjs\ni/ySfNhbyT3Tcv4aciPqHYWIkL1/PwK1tNB06BBMGAaWzs5QIYIxEaw0NKCgrY0wBQUUb90Ku5AQ\n6K9cCbU+fRDo64uhc+ZASVlZokwBn4/M2Fi49uvX5nlrKipg9JaMKBaLBXNra1S88Jqhl+ni4YHE\nsDCp9IEffYTUBw9Qz+dDa/p0mBw8iA5FRfAzMUFjx45QU1CAs7o6lIVCmLq4wEBXF3oNDciYMgV3\n7e1RGxHxVtokR86/jXR+OnoX9YY6Wx33LO/BRPGPLUAYYrCpeBO2lGzBfdf76KkpuWFmLjcXQx8N\nxXdO32GBzQKpsldFr0JNaw2Oex6X2FsKADJqM/Bp4Kfwm+An9TQeAKz2X42Pun6EHpbSr19JKU1B\nUHoQlgxeIiUDAN9gX8wdNlem7FHyI7jau0JLQ/aecXnFebCztJMpkyPnVciNqHcMYhgUHDiAEG1t\nVC1dCiOhEPoKCjDW1gafzUaRsTHsCwthExWFDEdHFLFYGLH6j/ddtXA4uHfiBMZ8/rlU2enR0bDp\n2BEa2tptnr+ytBRGZmZtyv8uZtbWKG/HiHL39JRpRKlqaGDwrFm4c/jw8zQVDQ1MPnwYJ/38oH/o\nEBxraqC9ezfSMzJgaGMDE2Vl6LPZ0CsoQGr//njg6IiakJC30i45cv4NnOKcwsCSgfhK7yv4mvhC\nlf2HV4cr5mJa1jQENgQiwi0CzmrOEnkTGhMwMHwg1jmtwyLbRS8Xje1J2xFQEoArw65ARUHy/XfN\ngmZMvjoZPw78ET1MpY2ku9l3EZQThO9HfC+z3t9e/hbfjP4G2mrSc1NpbSmisqMwqa/00iEABEYH\nYkTvETJlRISC0gK5ESXnLyM3ot4RxC0tyFq2DI/V1FD/xRcw4fGgo6QEvpkZbC9dQtnatQgFMCo8\nHKrm5mgoK8Plb77BvGPHoPDCaw7uHjsGdy8vGNtIxwDEBAWh2wsvj5VFaUEBLGxt32jbXsTMygrl\nRUVtyl379UNuUhK4HI6UbMwXXyDw8GHwXlgOdBs1CvZ9+uDKunVgsdlw/vJL2B44gEuNjTCOjob2\n8uVoUVKCsaIi9HJzUTR0KB4bG6Pk0CEwT/e3kiPnXaeZacaCigXYWrsVwZbBmK8zXyLmsYhfBI8U\nD2iyNRHSOQSmypLe5tCaUIyIGIG9rntlGlC7U3bjeNZx3B11F3oqkq+JEjNizPGfg37m/bDAXdp7\nVddSh3kX58F3qi+0VKS9RQ+zHyKxJBGfDvlUZtuOBB7BtAHToK4iHYgOAIFRbRtR1XXVUFVWhbZm\n2zeOcuS0y9t5bZ80/4+neq9ofPSIkrt2pQSA0gBKZLEoecgQOm1mRiErV5JYKKRHBw7Qdjs7qi8q\nIiIihmFo77hx9Pt330mUJRIKaYGNDWVGRso814LevSkmOLjNuojFYnJTVaUWLvfNNfAl9m3cSHvW\nr29XZ5mXF4X+/rtM2XZvb7q6a5dEWlNNDX1tYUGpd+8+TwvduZP+5+xMnPJyqklLoyOOjvRg9GiK\nd3SkVIBSAYpXVKSMiROptbLy7zfsP8r7Mu7f5XY84D4g+zx7mls+lzhijpT8dt1tMo0ypZ0lO6Ve\nAExEdLH0IhndNqLgKtlzg0+aD9ldtKOipiKZ8hXBK2jQ2UHUKmyVkjEMQ1NPT6Xl15fLzCsWi6nv\n1r504uEJmfJWQSuZfGxCqYWpMuVVdVWk7aVNAqFApjwiPoJ6Tu4pUyZHzuuMe7kR9S+kNT+fCmbN\nohQ1NUoHKElRkUJUVChi1ixK/PVX2mdoSGnnzhERUfTx47TN0pJqcnOf5w/av5829+hBQj5fotx7\nJ0/S2oEDZZ6zrqqKvLS1SfBSnhepLCujfsbGb6CFbXPp2DFaM2dOuzrnd+2i7QsXypTlxMbSPAsL\nErRKTtgpgYH0taUlNdXWPk8L2rKFfu7cmZqrq6m1oYH8xo6lM56elHf1KvlbW1OUnh6lstmUBlCq\niQlVbNlCopaWv93G/xLvy7h/F9vBE/Po66qvySzHjK41XZOSC8QCWlOwhiyiLSi0IVRKLmbEtCF9\nA1kFWlFcQ5zMc+xI3EG2F2wpj5MnU74neg+5HHWhOl6dTPmRyCPU+efO1CKQPa6O3D9Cfbb2IbFY\nLFP+273fyGuDl0wZEdGZO2do3OpxbcpPXTtF05dPb1Mu57+N3Ih6h+Dn5VHp559ThrExpQOUyGZT\nlIUF3XR2put9+1LFo0d094sv6FcHB6pMTCQiorjTp2mLmRlVZWQ8L6coIYGWGhpSRVaWRPlCgYAW\nOThQUkiIzPMHnD5NayZMaLeOcY8ekXfv3n+voa8g/O5dmj1kSLs6BenpNMHCQuZdMxHRxhEj6M6R\nI1LpZ5cto1+mTpXIF7BuHe3p2pWaa2qIEYspbP16+sXSkvLv3KH4rVvptL4+RQwfTjE6OpTMYlE6\ni0XZnTpRzf79JGpo+HuN/Q/wvoz7d60dQdwg6pDXgaaWTqVqUbWUPIeXQ/2T+tOo1FFUJaiSknOE\nHJoUOYkGhA2gCl6FlJxhGFoVtYo6Xe5EJc0lMutwOvU0mR8wp/yGfJnyyKJIMtpsROmV6TLl1Zxq\nMl5uTHEFsg04hmHIfZk73Yy+KVNORDTl2yl0/ObxNuVrf15L3x/4vk25nP82ciPqXwzDMMSLj6eK\n5cspy9SU0tlsSlJVpXAVFXo8ahQ9mDGDzpqYUObRo1SdkkLHu3Shq97exKt7ckf36JdfaKuFBZWn\npDwvk8fh0LqOHSn85Emp8wUePUrr2jFONsyYQVd//bXdOl8/c4aWT3+7d235WVk01M6uXR2GYcjb\nzo6y4uNlytPCw2m+tTW1vuQ1EvB4tKlrVwrcs0eiLP81a+jnzp2psbSUiIhyb98mH1NTClu/njh5\neXRv+nQ6b2VFid99R+GdO1OUmholKyhQhoIC5XXpQnX79pHwaV45krwv4/5daUepsJRmlM0g21xb\nmd4nMSOmfWX7yCDSgH4u/ZnEjLSHJ7EhkVyCXWhB/AJqFUkvwbWKWunj+x9Tn+t9qIZXI7MeZ1PP\nkqmPKaVUp8iUVzZVkvUP1vR7suxleSKij49+TMvOLmtTfiXiCnVd1rXNm6nmlmbS9tKmmgbZdSQi\nGr9kPPkF+LUpl/PfRm5E/ctgBALiBgVRxWefUbaREWWoqVGiigrFOTpSgJYWxc6dS7GrV9MZQ0MK\n/+wzaqmupsSjR2mfoSElHD78fLK49+OPtN3eXmIJTywW0/4JE8h3wQKp87a2tNB8a2tKDQuTWa+W\n5mYaoaNDdVXSd6QvcnDbNtq5Zs3f6IFXIxAIyE1VlVp5vHb1DqxeTQe/+aZN+Q+TJpHf9u1S6dX5\n+bTcxIQyQiWXL573aU4OERE1lZfT+eHD6YyHBzUWFlJZaCj97uZG/kOGUN7x4xQ+dCjd09eneHNz\nSlZWpkwVFcpzc6PaH38kfrrsO+v/Iu/LuP+3t4Mn5tFPtT+RQbYBfVv9LXHF0nGLWS1ZNDB5IPVP\n6k8ZLRlScoZhaH/efjK8bUgni6RvxIiIyrnl1O9GP5oSPIWaBc0ydc6nnSdTH1NKqkqSKW/mN1Pv\n/b3pu4DvZMqJiPxi/MjhGwfi8KRjuIiezHfuy9zp6uOrbZcR4kfDlw1vU05E5OjlSGk5ae3qyPnv\nIjei/gWIGhqIc/48lX34IWVpaVGmkRElqKlRopsbPe7dm27r61PKqlWU4eNDF2xt6e748VSfnk5N\nZWV0ecIEOu7mRtWpT4ImxSIR3Vixgn7u1Om51+QZV9avpx8GDJCKgyIiOrtxI2339m6zjkHnz9OK\nkSNf2ZY1c+bQRRnLZG+a0S4ulPF0ybItMuPiaIqtbZt3oSUZGTTTwIAaq6WXMlICA2mFqSlVFxRI\npEccPEhbzc2pOCaGiIgYsZgeb99O+wwNKfHYMRIJBJTm40NnTUwodNYsKrlyhSLHj6c7RkYUP3Qo\nxRkaUqqREWXp6lKegwNVrV5NLY8eEdNGPMd/gfdl3P9b2yFmxHSy8STZ5NrQ+JLxlM6XNuCbRc20\ntmAtGUQa0K7SXSRiRFI6JS0lNPbxWOp5vydlN2fLPFdkVSRZnbeiTXGbZHqwiIj2xuwl8wPmlFgp\ne/wKRUL64PgHNOfCnLbHbl0JGS83pse5j9tqNl0Iu0DdV3RvswwiIu913nToyqE25S28FlJ1UyWB\nQHbQuRw5ciPqH0JQWEh1+/dTsZcXZWloULaDAyUaGFCcgwOlTp9O993d6a6DA4DSXeEAACAASURB\nVOXu20cFly/TtV696GqPHlQWEkIMw1CSry/tNzKiB999R8KnAdKtHA75jh1Lvw4dStw6ySDNhydO\n0CobG2qokI5dqMjLo5n6+lRVWNhmfb+ZOJFu+fq+sl1TevWiuEeP/lxn/AW+mDyZbp0/364OwzA0\no0MHSnnc9kR7ZNky2jt3rkxZ4J499G2HDsR5ychKvnyZNhsaUuLFi8/TKhMS6ES3bnTBy4saCgpI\nwOFQ3MaNT+Klli6lypAQivnwQ/LX1aWYDz6glNGjKVpDg9IdHCjb0pJyTEyoYtEiavb3J3Gr9PLI\n+8z7Mu7/be0QM2K61nSN3AvcqV9hPwprkfYyMwxD56rPkWW0Jc3MnEmlfOklZzEjpkP5h8jwtiGt\nT19PfLH0TZiYEdOPiT+S8RljulJwpc36rApZRR2OdGgzBkooEtLMczNp1LFRJBDJNlz4Qj4N3D6Q\nvr/edpwSt5VL1vOtKTRZOhj+GRW1FaQzQocamtqOW4xOiibXsa5tyuXIkRtR/08wQiG1hIVR9bp1\nVNC1K2Xr6VFu9+6UZGND0WZmlLVwISV98gkFmJjQw0GDqOzKFSq6eZOu9+lDlzt1otzz54kRi6k2\nK4sujhxJvl27UkXcH8GU1dnZtMvNjS4vXkyil+6aYq9coRWmplQmYwmJYRjaNGoUXdiypc2611ZU\n0EhdXWp6RZC0WCymbpqa1Fhf/yd758+ze9062rdx4yv1fLdsafMpPSKiFg6H5ltbU/wLWxu8iN83\n39CWXr2I19QkkV4aH08/WFtT4MaNz58KEgkEFPHDD7TP0JBi9u0jsVBILZWVFPHll3RaX5+iVq+m\n+sREyti0ie6Ym9NDDw/KXLqUUseMoShNTcrs2pVyXVwoW0eHSqdMoYYjR0hQXPz6nfKO8r6M+39L\nO4SMkM40niHXfFfqVtCNrjRdkfLGMAxDAXUB1COhB3VL6EYPGh/ILCu+IZ48wzypz4M+lNyYLFOn\nuLmYhvoPpYG3Bra5hUEdr47G+o0ljzMeVNMiO/5IIBLQ9DPTacSREcTly94ihWEYWvTbIhq3d1yb\nT+MREa0/s56m/W9am3Iioh2nd9DcrbJvoJ7hc9qH5n87v10dOf9t5EbUW0RQVEQNR45Q6ZQplK2r\nS3mdO1Pe0KGU3LkzRerrU/aCBZT7/fcU7uVFtw0MKHn5cmpMTqbCa9foet++dLlTJ8q7cIEYsZj4\nHA6FrllD+wwM6PGOHRKGUvy5c7TZ0JAeHTggNVkm+fvTMiMjKoiNlVnH2wcP0ooePUjYjrv6xNat\n7RoizyjOzycPc/PX7J2/x7VTp2jp1Kmv1KspL6eRurrU+MK2BS8TffMmLbK3J25jo5SMYRjynT+f\ndgweTDyOZOwFp6KCfvHwoCNeXsQpL//jnGlpdG7oUDrWuTMVBAUREVFTYSE9+uILOqWnR48+/5wa\nc3Ko5MIFCvP0pDvm5pSybBkVbNpEyZ6eFKWnR1menpTv6UnZ+vqU7+pKVStXEjco6L30Ur0v4/6f\nbkejqJH21+0nhzwH8ijyoNvNt2UaTyENIeSZ7Ekd4zrSxeqLMpfdSnmlNC9uHpkEmNAv+b/IXN4T\niUW0N2UvGZ4xpK3xW0kkltYhIoqriCP7X+1pefDyNr1LTa1NNM53HI05PoZ4grZjHXcH7qbO33Wm\nxhbpsfqMhLwEMpxlSEVVsg06IiKRSEQOUx0oPCm8TR0iotmrZtPhC4fb1ZHz30ZuRL1BxBwONd++\nTVUrV1J+586Ura9PRaNHU97EiRTfqRNFGRtT9sKFVLR3LyV98QUFmJhQmIcHFZ06Rbzqako7cIAu\nOTnRtZ49nxtPYqGQEo8dowPm5nRrzhxqKit7fj5eYyP5LVhAOxwdqSRO+hHfyPPnabmxMeVERMis\nb3F6Os0yNKTidoKcBXw+TbS0bPMptxe5fekSLRnX9n4rb5KctLRXPqH3jE0zZ9KZnTvb1TmweDHt\neGlrg2eIRSI6sXAhbe3Th5pfWiYVCYV0Z/162mJqSun+/s/TGYahzN9/p0N2dvT7pElU+3SLCW55\nOUWtXk2n9fUpdPZsqoqKosbkZEr5+msKMDGhB/37U8727VS8fTslDx5Mj7W1KWPIECqcMoUKevWi\nbC0tKhk7lup9fKg1La3deI93hXd93D/jn2pHamsqfVbxGell65F3qTc94Ep7lYSMkM5Xn6deib3I\nKdaJfCt9ScgIpfSqWqtobdpa0vfXpzWpa6hBINv7HFkVSb2u9aJBtwZRer3s+UMoFtK2R9vIcJ8h\nnU9re+m9qL6I3He70ycXPyG+sO096I7cP0LWq6wpvzq/TZ1WQSu5felGJ4Jlb7z5jDN3ztCAJQNe\nOX6cRzpTYkb7sZdy/tvIjai/gbi5mZrv3KGqb76hwr59KUtDgwo9PKh45kzK8famWHt7irGxobwV\nK6ji3DlK37iRgpyd6a6DA6Vv2EBNmZnUkJlJUatW0RlDQwqaNIkqwsKIYRgSi0SUevo0HXZyonND\nhlDpS4ZQ2s2b9IOVFV385BNqfclDwjAMBe7ZQ1+Zm1NRG8HXTXV1tMTJiQKPHm23jVcOHXqtgHIi\noh0rV9IvW7e+lu7fRSwWUy89Pap6wQPUFukxMTTBwoJ47eyizufxaFnXrnR9716ZcoZh6PxXX9F3\nnTpR5dOn814kJySEfrC2pvMff0zNL8RQCXk8ivjxR9pnaEg3Z8+muuwnAbmtdXWUtHMnXbC1peu9\ne1P2qVMkaGqi8uvXKWryZLqlrU1R3t5UeOQIlf3yC6WNHUuPtbQodfBgKvj4YyqeNIlybW0px8SE\nyqZPp/qDB4mfnv5OGlXv2rhvi//PdtSKaulg/UHqX9ifzHLMaEP1BioRSO/FVCmopJ0lO8k2xpY8\nkz3pau3VNj1PX6V8RXr+erQkYQkVcAukdIiIshqyyDvYm8zPmdPxzONtft8SKhOo12+9yOuCFxU2\nth1rGZgVSOZbzWlnqOyd0J9x9MFRsvjagrIqstrUYRiGFuxfQFO2T2m3LLFYTJ1mdqKAxwFt6hAR\nlVWWkW5PXRKJZHvY5MghkhtRfwphVRU1Xb9O1WvXUmH//s+NprIlS6hwyRJKGTqUIjQ1KXnwYCr+\n8UeqOH+e0jdsoBB3dwowNqakL7+kusePSdDcTNknT9KtgQPpjLExRa1aRY1Pf1wFLS2UcPgwHenY\nkU73708FL71ipTY/n05Pm0bb7e0p++lS0YvwW1ro6Mcf00Z3d6rKk71DsKC1ldYPH05Hl8t+jcIz\nWltaaJK1NSW34cl6mQ89PCi8jdiit8GC0aMpsI1Xu7zMOm9vOiVjO4MXKc/NpTlmZvTo8mWZcoZh\nKPjAAVpubCzxephntDY10Y0VK+h7Y2OKOnqUxC9Mvq0NDRT+/fe0z8CAbn78MVUmJBDREy9X4fXr\ndNvLi84YG1PEsmVUHRtLvKoqKjhyhB6NGPHEoJoyhYp8fany9GnKWbSIYqytKcbamrJnzKCSL76g\n0o8+olxra8oxNaWyGTOo/sAB4sXFESOU9jb82/i3j/vX5W23gyPm0EXORZpUOol0snVoWuk0utF0\ngwSM5BKZiBGRf50/TU6fTDqPdWhu1lx6zJF+uIJhGAqrCaMZMTNI11+XliUvo5IW2ZtiptWn0bwH\n88jgtAFtS9hGXKHsG5LK5kpaFLCITHxM6HDC4TaNGZ6AR1/d+Iost1lScHbbr5FiGIY2Xt1Idqvt\nKLM8s009IqL9N/dT5y86E6dF9pYHzzh6/Sj1W9TvlTccx/2O09Slrw4ZkPPfRm5EtQEjFBIvNpbq\nDxygslmzKM/BgbJ1dKh4xAgqW7yYij7/nDImTqQoY2OKsbWlnMWLqerCBSr386PkZcso0MaG7trb\nU8pXX1HNgwck5HKp8Pp1Cp05k07p6NCd0aMp//JlEj3dbqCprIzCNmyg/cbG5PfBB1QQFCQxyLm1\ntXRr1SrapK9PdzdvJr4Mr0pBXBytd3WlQzNmUGuz7P1ZBK2ttGXcOPpx8mQSveIH9vD69bSunW0P\nXqSpsZG6aWoSt43zvg1+2bqVtr3CEHxGQUYGjTE0pKpXbHiZExtLs42MKO7OnTZ10u/doxVmZnRh\n5UoSyNirqjgmhn7x8KBdrq6UdvOmxHXk1dfTo23b6ICFBZ0dPJiyrlx5bmw1ZmdT7IYNdMHWli53\n6kSJ27dTY24u8WtqqPDYMYoYNYpuampS+LBhlLVzJ1Vdv06lu3ZR6siRFKGpSYk9e1Le/PlU+sUX\nVDpzJuV36kRZmppUNHAgVa1eTZzffyfhC8vB/xb+TeP+7/A22lEuLKdf63+lMSVjSCtbi0YUj6Cj\nDUepQSS5zCZkhBTcEExLcpaQSZQJ9UrsRb+W/0qNQunYoXJeOe3N3UtdQrqQc5Az7cndQ/UC6YdB\nGIah0LJQmnB3AhmfMabv479vc+PMmpYa+u7Bd2Swz4BWBK+gel7bD5fczrhNjjscyfuUN9U0t73J\nZT23nqYcmEI9v+9JFQ3STxW/yG/3fiOLeRaUW57brl51fTUZf2BMcZmydzh/Ee+l3uR72feVenL+\n27zOuGc9VXzrsFgs/D+dSgKmpQX8pCTwExLAj49/8pmSAiVbW6h07w4YG0PIMGjJywPn4UMo6utD\ne9AgaHl4gKWvj4bkZFQHBaEhOho63bvDeMQImE6cCGVzc5QHBaH41i0U3bgBPVdX2E+fDpspU6Bu\nagpRaytybtxAyokTKH30CC4zZqDHsmUw6Njxed0aS0oQtns3Ynx94ebtDa/Nm6FtZiZRfz6Xi9s7\ndiD00CFM37ULfWfOlHj7+jOaGxrw0/TpUNHQwKoLF6CopNRmn2QnJGC5lxdOJCTAyMLilX1498oV\nnDt4EMcDA/9Ez/89MpOS8OmECQjOy5PZ3pc5unEjMmNj8b8bN9rVT3v4ENunTMGCPXsw8MMPZeo0\nVVfj5JIlqMjMxKxffkGHgQMl5ESE9Js3EfDNN1DS0MCgVavgOnky2AoKAACxUIhMPz/E7t2LppIS\ndJ41C51nz4Zh584ghkFleDhyT51C4bVrUDMxgc2kSbCZOBHajo6oDQlBpb8/qm7fBgAYjxoF/f79\noaqlBX56OjgPHqApIgIqNjbQ6tMHqvr6UGhthSgnB62PH4OtqQmVbt2g0rUrVLp2hWrXrlC0tX2t\nPnwb/FPj/k3zptvRKG6EY4EjvNS9MEFzAkapj4KOgs5zea2wFsGNwbjTcAc36m7ARtUG3gbemKI/\nBY5qjhJl1fBrcKPyBs6WnkV0fTTGm47HbMvZGGY0DGwWW0K3vKUcJ3NO4mjWUSizlfFpx0/xifMn\nUFdUl6pjTn0ODiUcgm+yLyY7T8bavmthr2svsz3xpfHYeHcjUitTsX/CfozpOKbNtgenBWPBbwsw\ntstY7Jy2E6pKqm3qHrx9EFsubEHwlmC4WLm0qccwDCZ/Oxn25vbYtXRXm3oAwGnmwGaIDTIDMmFs\nYNyurpz/Nq8z7t8bI4rh8yHMzoYgPR2CjAwI0tLAT0iAsLAQyi4uUHJxAcvQECIAvIoKcBMSwC8p\ngUbXrtDs3RtqLi4Qq6iAk5WF+ogINERHQ83aGkZeXjDy8oJu375ozM5GeXAwiv39UZeYCFNPT1iO\nGQObSZOgYWEBQXMzCgIDkX31KnJv3YJJt25wnTsXTpMmQVlD40k9GQZ5ISGIPnYMmQEB6Dl3LjxW\nrICulZVEe0RCIR6fPo2r69fDeeBAeP/vf9C3tJTZ9sKUFGyfMgXdR47EvJ9/bteA4tTXY1GfPvhk\n0yaM+Oij1+rb5dOno/egQfjos89e72K8AYgII52dsev8ebj26PFKfaFAgMX9+mHY9OmYuXp1u7qF\nKSn4fswYeEyfjlnbtkFJWVnm+aMvXoTf6tWw6dkTU374AaYdOkjoMAyD9OvXcX/nTjRVVKDvkiXo\nNmuWhCFcnZKC1FOnkHb6NDRMTeE8eTIcx4+HoasriGFQ/fgxCq9eReGVKxDzeDAbNgzmw4bBbOhQ\nUFMTqgIDUfvgAerCwqCoqQn9gQOfG1XisjI0x8SgOToaoupqaHTrBg0nJ6jo6IDN40FcVAR+YiKY\npiaouLtDxd0dyi4uT46OHaFgYvLWjSu5EdU2YhJDgfXE8K4X1SOqKQphTWEIbAhEJi8TntqeGKE7\nAuP1xsNW1VYiX0JjAvwr/eFf5Y+0pjQMMxyGDy0/xAfGH0gZRMXNxfi98Hf8Xvg7EusS4W3rjQXO\nC9DHqI/U9ecKuLiVdwtHEo8goSoBc1znYGmPpbDWtpaqPxEhLD8Mux/uRmRRJL4Z8g0W9V7UplFU\nVFuENX5rEJEbAZ+ZPhjrPrbNvmkVtOLr418jJCUEN9bdgIOZQ7t9ufXEVtx8dBP3fe5DRVmlXd1D\n5w4hKCIIfvv82tWTI+e9NqJa4+LQdO7cE4MpIwOi4mIo2tlByd4eLCMjkJISBHw+WsvL0ZKWBnFT\nE9Q6d4Z6ly5QcXAAo6aG1vp6NKWkoDEhAfyKCuj26gX9/v2h168f1BwdwcnPR+XDh6h8+BA10dHQ\ndnSE6aBBsBw9GqaDBoGtrIyqhAQUhYai6N49lISFwbxvXzhNnAjH8eOh9dToYRgGJVFRSL12DYnn\nz0NVRwe95s9Ht5kzoa6vL9GuloYGhJ84gbu7d8PQ3h5TfvwRDn37yuwDPo+HKzt34tb+/Zj3008Y\nOmdOu33G5XCw3MsL7p6e+OKnn16rn2urqjDS2RnB+fnQ0dN7rTxvCp/Nm1FRUoKtR468ln5VSQkW\n9+uHBd9/jw/mzWtXl1NTg33z5qG2tBSL9u+Hy4ABMvUEPB7u7t6Nu3v2wMnDAyO++gqOAwZI/fgU\nPX6MqKNHkXL5Mqz79UOXadPQ8YMPoGlkBABgxGIU37+PnGvXkHP9OgDA/oMPYD14MCwHDoS6kRE4\nOTkoDw5GWXAwykNCoGJgAON+/WDUuzcMe/eGkrIyGiIiUPvgARpiYtBaUgJtNzfo9OgB7Q4doKSo\nCHFVFVoSE9GSnAxBWRlUnZ2h7ugIFR0dKAJAczPEpaUQZmaChEIod+z45HBxgd7q1W/cqJIbUbLh\niXk4XXMaj5seI6IpAsX8YvTU7IkB2gPgpeOFflr9oMx+Yty3iFoQ3RCN8LpwPKx7iEd1j2Cqaoox\nxmMwxmQMPPU9oaLwh+HAEXDwoOIB7pXfQ3BZMEpaSjDeejwm20yGl7kXVBUljZzqlmoEFwbjctZl\nBOYHoo95H8x1nYvJzpOldAGguKEYl1Mu43DkYQDAZ/0+w/xe86GmpCazrdmV2fjpzk/wi/HDp0M+\nxdoxa6GhotFm3zxMe4iFBxbC1doVRz4/Al1N3Xb7ctf5XThw+QDuH7gPS2PZN5rPEAgEcB3nikOb\nD2Fo36Ht6sqR80aMqICAACxfvhxisRgLFizAmjVrpHSWLl2K27dvQ11dHSdOnEC3bt3+UmX+DI2X\nL6Px3DmIAAi5XLRWVaE1NxcAoOrgAFVHRyiYmIDU1CBiGPCqqsDNyUFzWhrYqqrQdneHdpcu0OzY\nEWxdXQgFAtQnJ+NecDCsiosh4nKh37UrTAYMgImHBwz79oWgqQmV8fGojItDZWwsSsLDoWluDuvB\ng2E1eDBsvbygqqsLIkJtbi4KwsKQ9+ABsgICoK6vj04TJsDN2xvm3bpJ/Fi1NjUhLSgIkWfPIjUw\nEK6jRmHE11/DvndvmW1v4XCwb/16ZPr5oUPfvpi/Zw+MXvJkvUxhRgbWTZmCHsOGYfneva/9Y7lt\n2TIQEb7bt+/1LowMQkNDMXjw4D+dr762FiOdnOAXHQ1rh/bvRJ9RkJ6Or0ePxqjZszFvw4Z2vXJE\nhLDz5/Hb6tVw7NULFsOG4ePPP5epy+dy8dDXF/d8fMCIROg3eza6TZoESzc3ib4UcLlIvXoVKVeu\nICcoCCadOsF55EjYenjAqk8fqGhqgohQk5KCvNu3UfLgAUoePoSGmRksPTxg0r07TLp1g6GrK5pz\nc1EVGYnqyEjUREWhKS8Peq6uyNfXx5Dhw6Ftaws2w0BQXIzGuDg0JiaCm5MDVVNTaHbsCA1HR6hq\na4MtFoOpq4OgqAitmZkQlJVB2cICqlZWUNHTg6KSEhQUFGB57tx7aUS9iTnsTbdDyAixMHchemv2\nRj+tfnDTcAMIKOYVI605DYmNiUjkJCKJk4SClgJ00e4CDwMPeOh7oL9ef5iomgB4YmCl1KcgpiYG\nMTUxiK2JRXZUNvoP6o+hZkMx1Gwoehr2hCJbEcCT73wRpwixlbF4WPIQwYXBKGgsgKeVJyY5TcIE\nxwkwVDeUrKtYiJiSGITkhuBa2jXk1OZgnMs4zOs5DwPtBsr8zlQ3VeNW0i2cCD+B9PJ0LPBcgBVe\nK2Co9UfZL84LRITIrEhsu7QNifmJ+GneT5jmMa3dPmxuacbyvcsRnhyOO7vuwNpU2lv2Mj8d+wl3\nH93FnWN3Xqkrq57/Zt6VegLvTl3/thElFovRoUMHBAUFwcLCAr169cK5c+fg4vLH2rS/vz98fHzg\n7++PyMhILFu2DI8fP/5LlfkzVJ49iypfX0BDA6SoCJFIBD6XC35VFVpLSyHm8Z78iNjYQNnICGxt\nbUBFBWIALdXVaMrJQWN2NgQNDdCyt4eOkxP03N1xIikJXy1ciNbmZjTk5KA+Oxv1OTmoTUsDW1Hx\nyY/c08NiwAAoamigLi8PFcnJz4/SuDiwFBRg5+kJu4ED4TR8OAydnJ7XvaG8HIWxsSiIiUFGSAgK\n4+Jg36cPek6dil7TpkFDhsenub4eSffuIfrGDURevYoqMzMcOnUKjj17tttP1WVl8Nu3DzeOHsWS\nH3/E+IULX7uPQ2/dwvqFC3E1IQEGxn89dmDTpk3YtGnTX8p7Yvdu3Dp/Hr/duwd1jbbvXl+ktqIC\n2+bORW15OT7ZtAmeEyaAzWa3qd/K5SLo+HFs2rgRA6ysMGDqVPQePx7Wrq5S+YgIBTExeHz6NBJv\n3oSAx0PnESPg0LcvbHv2hIWbG5RUnngFRHw+8u7fR+69eyh4+BBlCQkw6tABpl26wNTNDWZubjB2\ncYGGsTFqU1NRGh6Oyvh4VMXHozYjA9o2NtDv0AF6jo7Qc3KClrk5iMfD7hMnMNvREfWpqWhITYWQ\ny4WWrS007eygaW39xHAiAjU3Q1BRAX55OVqLi8GvqICysTHUra2hamAAZTU1sBgGrNZWQCCA653X\n/2F5Xf5pI+pNzWFvwxN1sOAgcrm5T46WXBTzimGsYoyOmh3hru2OLtpd4KbtBn0FfVS3VqO0pRQF\nTQXIbMxEFicLmY2ZqGqtQkedjuhp2PP5cW3/NWzauAlFnCLkNOQgpz4HOQ05SK5ORlxlHJTYSuhh\n2gN9zfpimM0w9DTtCSWFJzcbrcJWpFelI6kiCckVyYgvjUdUcRQcDBwwyH4QxrmMwyD7Qc/1n1He\nUI7ogmhE50cjOD0YqWWpGOYyDDP7zsQ493FQVpReMt+4cSPGzx6PO/F3cOb+GQjFQnwx5gssHrUY\nKkptL8k1cZtwOvA0tp7YihG9R2D/iv3QVNd8ZZ/73/fHJ99+gvBz4XCwfr2bMuDvzV//n7wr9QTe\nnbr+bSMqIiICmzdvRkBAAABg+/btAIBvvvnmuc6SJUswZMgQTJ8+HQDQsWNH3L9/HyYmJn+6Mn+G\n7L17Ufjrr2CrqwPKyiA2GyKGgYjPh4DHA5/DQWt1NRRUVaFiaAglXV0oamqCraYGtro6WGpqYNhs\ntDY1gVtRAW55ObgVFQhisTDF1haaVlbQMDWFir4+FLS0wFZVhUgkQkttLRqKi9FQVISGwkIIeTzo\n2drCpHNnGLm4QN/BAZpmZmCpqIBbW4vmmhrUl5SgOi/vyZGbC7FIBNuePWHTowecPDzQYdAgKKur\ng9/SgsaqKtSWlqK2tBTVhYUoSEpCQVISKvPz4TJgAHqMHg3PGTOw5+BBqS8hwzCoLi1FYUYGshMS\nEH33LjJiYjB8xgzM+e47GJmbv1bfVpSU4NLRozh38CB+uXYNXdtYTnxd/s6AISKsW7AAKTExWLl9\nO/oOGwZlGTFMsvLdv3IFZ3bsQFVJCTzGjYNr//5wdHeHuZ0d1LW0pO6gN27ciGnDhiH80iXE37mD\nxupqOPfpA6tOnWDh7AxTBwfomphA28gI2oaGUFRSQmVODtKDgpAfHY2CmBhUZWdDz8oKxg4OMLK3\nh4GtLbSNjaFlZAQ1bW20VFejsbAQtVlZqExLQ3VGBlrq6qBjYQFdGxvoWltDy8QEqjo6YAmFEPN4\nEHI44FVWormsDM0lJbhWUYEPtLWhaW4ODTMzqBsYQFFBASyRCMTng2lpgbChASIOB4KGBgjq6sBW\nUYGqoSFU9fSgoqYGBTYbLLH4eR4wDIYlJ/+la9Qe/7QR9abmsDfdjhZhC2bEzIA2WxvqLHUokRKI\nIXAEHNTz61HLr0VZSxkqeBXQUdKBiZoJDFUMYaBsABNVE+gp6UFHQQfKLGXU8mpRwa1ABbcCldxK\nZF7MBH8IH8bqxrDTsYONtg0sNS1hpmEGczVzsMBCXUsd6lrqUMWtQlFDEQrrC1HUUIS6ljo4GznD\nzdQNriau6GTcCW4mbhCLxahprnlyNNWgpL4EudW5yK3KRU5VDgRiAXrb9UYv214Y6DwQA50HQkVJ\nBUKREPXN9SirK0NpXSlKa0uRXpKOpIIkRFyLgLWHNUZ0GwHv/t7w7OQpNSbFYjHKa8uRVZyF5Nxk\n3Iu9hweJDzC422Csnb0WvTvJ9tY/g2EYJGcl45jfMVwKuITL+y6jf/f+f+pavSs/+O9KPYF3p66v\nM+4V2xOWlpbC6oVlIktLS0RGRr5Sp6SkRMqIetNc2rIF2rW1IEDiED/9ZACIAJBAAOJwnqfRC3K8\nkMY8PTgAMjIzQZmZEumyDvHTMsrS05Gang68VJaELosF8dM8DIC8oCCw3fbsEgAACA9JREFU7t4F\nALBeuEjEYoFhsZ58stlg2GyInv5d+fAhQh8+xM/r1qFAIEDozp1P2iAWgxGJQGIx2EpKUFRVhaKa\nGlS0taFuZ4eI6GhETJjwR+e9eL4X/mYYBhUlJWDEYnhNnoxLUVGwsLH5s5fmjcJisbDt6FHcPHcO\n+zdtwheTJ8Pc2ho6+vpQUVWFsooKWDK8TM8mY5ahIXRVVRFx/z5CbtxAK4cDIY8HIoKisjLYiopg\nsdlgs9nI4/MR8csvT/IBAJuNyIgIRD18+MTgEIufeG4Y5kkfPpvwn14vsFgAEcpzc5GZmwsFIigA\nYBOB9fST/bTsZz8Vz76PFfn5UMjPh8JTGfulg/XC0QygtL4e7Pp6sFJTJeSy/gYAtlAIheZmKBQU\nPC8TL+gRgGF/60r9O/m3zmFROVG4EX9D9qT04iQiBmpQgxqqkdSll/Re/LsBQCVQjGIUU7F0mS9P\nUi9OTASklKcgJTnlj8rKyisGWGKWRN67eXdxF3exjbUNxCKJ/Gxig0UssBgWWGIW2Awb4iYxStJK\ncDztOI6dOSahLyIRxIwYYhJDka0INUU1qCqqQktZCzaqNijJKsHn6z8HQfZcBgDcFi6KyotgbmwO\n75HeSLyWKH8aT84bp10j6nXjI17+8raV7596zPrP8uhtFPqyNduWdUv0h0wslq3zlEKhUDpRKHxy\nNDUBVVV/oaJPiD52DD8cO/Zqxddk8+bNb6ys5KysN1NQa6tUUh6P9/r5n12nf8DT8ja+o0vfkfH5\nZ3iTc9i7Mn8BAMLf/ileNGBk/f9FmOcWoiSiAtErzyOCCE1P/1Wj+s9VEkAucrEjaAd2rNrxp/M+\n403OX2+Td6WewLtV1/Zo14iysLBAcXHx8/8XFxfD8qXH7F/WKSkpgYWMvYf+6eBSOXLk/Pd4U3OY\nfP6SI0eOLNqOtgXQs2dPZGdno6CgAAKBABcuXMD48eMldMaPH4+TJ08CAB4/fgxdXd23vpQnR44c\nOa+DfA6TI0fO26RdT5SioiJ8fHwwcuRIiMVizJ8/Hy4u/9fe3bxE1cVxAP8K6iIFV4oLo0sz1Izi\npIH4AoELR8WXhSQRioJD0SpQkcSNQuALWoQmj7lQN/4Dgii4iEERIyMXQiCCE2LZKxZK0XX020Ix\nHpoZnfE+99z78Pvs7sDlfBfDdw5zzz3HjdHRUQDAvXv3UFFRgZmZGTidTiQlJWFiYsKU4EIIcRrp\nMCHEfyqKY2TObWhoiC6Xi1lZWXzw4IGZQ8fk0aNHjIuL49evX1VHCamtrY0ul4sej4c1NTX89u3b\n6TeZaHZ2llevXqXT6WTfKQcEq7S5ucni4mJmZmYyKyuLg4ODqiNFFAwGmZOTw6qqKtVRwtrZ2eHN\nmzfpcrnodru5dMaDrq3OTh1m9f4ipcOMIP1lvGj6y7RJ1PPnz1lSUkJdPzqV/NOnT2YNHZPNzU2W\nlZVR0zTLltDc3BwPDg5Iku3t7Wxvb1ec6I9gMEiHw8FAIEBd13nt2jW+efNGdayQtre3ubKyQpLc\n3d3llStXLJuVJB8/fsy6ujpWV1erjhJWY2Mjx8bGSJL7+/uW+3GMhZ06zA79RUqHGUH6y3jR9FfE\nNVFGGhkZQUdHBxKOd5BOPT4Ow6paW1vR39+vOkZEXq/3ZDPI/Px8bG1tKU70x8uXL+F0OqFpGhIS\nEnD79m1MTU2pjhVSeno6cnJyAADJyclwu914//694lShbW1tYWZmBnfu3LHsYufv379jYWEBPp8P\nwNEjtZSUlFPusj47dZgd+guQDjOC9Jexou0v0yZR6+vrmJ+fR0FBAYqLi/Hq1Suzho7a1NQUMjIy\n4PF4VEc5s/HxcVRUhD853Wyh9t559+6dwkRn8/btW6ysrCA/P191lJBaWlowMDAQcQd21QKBAFJT\nU9HU1ITr16/j7t27+PHjh+pY52aXDrNjfwHSYUaQ/jq/aPsr4sLyaHm9Xnz48OGvz7u7uxEMBrGz\ns4MXL15geXkZt27dwsbGhpHDRyVS1t7eXszNzZ18pnLGHC5nT08PqqurARxlTkxMRF1dndnxwrLV\nnjrH9vb2UFtbi8HBQSQnn36MhNmmp6eRlpaG3Nxc+P1+1XHCCgaDeP36NYaHh5GXl4fm5mb09fXh\n4cOHqqOdyi4dZpf+AqTDzCL9ZYyo+8uM54skWV5eTr/ff3LtcDj45csXs4Y/s9XVVaalpVHTNGqa\nxvj4eF66dIkfP35UHS2kiYkJFhUV8efPn6qj/MvS0hLLyspOrnt6eiy7MJMkdV1naWkpnzx5ojpK\nWB0dHczIyKCmaUxPT+eFCxfY0NCgOtZftre3qWnayfXCwgIrKysVJjKGHTrMbv1FSocZQfrLONH2\nl2mTqGfPnrGzs5Mkuba2xosXL5o19LlYeWHm7OwsMzMz+fnzZ9VR/rK/v8/Lly8zEAjw169fll2U\nSZKHh4dsaGhgc3Oz6ihn5vf7Lf12y40bN7i2tkaS7OrqsvybbGdhxw6zcn+R0mFGkP4yXjT9Zejj\nvEh8Ph98Ph+ys7ORmJh4srmd1Vn5L9379+9D13V4vV4AQGFhIf45PvtNtXD781jR4uIiJicn4fF4\nkJubCwDo7e1FeXm54mSRWfm7+fTpU9TX10PXdTgcjv/F3kt27DArf0cA6TAjSH8ZL5r+iiMtukRe\nCCGEEMLCrLtEXgghhBDCwmQSJYQQQggRA5lECSGEEELEQCZRQgghhBAxkEmUEEIIIUQMZBIlhBBC\nCBGD3yqoxN/MSjbTAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 83
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The **stats** module of SciPy contains more statistical distributions and further tests such as a Kruskall-Wallis test, Wilcoxon test, a Chi-Square test, a test for normality, and so forth. A full listing of functions is found [here](http://docs.scipy.org/doc/scipy/reference/stats.html).\n",
"\n",
"For serious statistical models however, you should be looking at the [**statsmodels**](http://statsmodels.sourceforge.net/) package, or the [**rpy**](http://rpy.sourceforge.net/) interfacing package, allowing R to be called from within Python."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fast Fourier Transform\n",
"\n",
"FFT is commonly used to process or analyze images (as well as sound). Numpy has a FFT package, `numpy.fft`, but SciPy has its own set of functions as well in `scipy.fftpack`. Both are very similar, you can use whichever package you like. \n",
"\n",
"I will assume that you are familiar with the basic underlying theory. That is, that any periodic function can be described as a sum of sine-waves of different frequencies, amplitudes and phases. A **Fast Fourier Transform** allows you to do this very quickly for equally spaced samples from the function, returning a finite set of sinusoidal components with `n` equal to the number of samples, ordered by frequency.\n",
"\n",
"Let's do this for a simple 1D function."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import scipy.fftpack as fft\n",
"\n",
"# The original data: a step function\n",
"data = np.zeros(200, dtype='float')\n",
"data[25:100] = 1\n",
"\n",
"# Decompose into sinusoidal components\n",
"# The result is a series of complex numbers as long as the data itself\n",
"res = fft.fft(data)\n",
"\n",
"# FREQUENCY is implied by the ordering, but can be retrieved as well\n",
"# It increases from 0 to the Nyquist frequency (0.5), followed by its reversed negative counterpart\n",
"# Note: in case of real input data, the FFT results will be symmetrical\n",
"freq = fft.fftfreq(data.size)\n",
"\n",
"# AMPLITUDE is given by np.abs() of the complex numbers\n",
"amp = np.abs(res)\n",
"\n",
"# PHASE is given by np.angle() of the complex numbers\n",
"phase = np.angle(res)\n",
"\n",
"# We can plot each component separately\n",
"plt.figure(figsize=(15,5))\n",
"plt.plot(data, 'k-', lw=3)\n",
"xs = np.linspace(0,data.size-1,data.size)*2*np.pi\n",
"for i in range(len(res)): \n",
" ys = np.cos(xs*freq[i]+phase[i]) * (amp[i]/data.size)\n",
" plt.plot(ys.real, 'r:', lw=1)\n",
"plt.show()\n",
"\n",
"# Can you then plot what the SUM of all these components looks like?"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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yxekOiMjtON0BERG5CRM7yhZ77IjI7dhjR0REbsLEjrLFxI6I3I6JHRERuQkTO8oWEzsi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\nEEIIIW4OAztCCCGEEEIIcXP+H5B7al4eHWAKAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 84
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of course, there is a short-hand function available for reconstructing the original input from the FFT result:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# ifft = inverse fft\n",
"reconstructed = fft.ifft(res)\n",
"\n",
"plt.figure(figsize=(15,5))\n",
"plt.plot(data,'k-', lw=3)\n",
"plt.plot(reconstructed, 'r:', lw=3)\n",
"plt.show()\n",
"\n",
"# Note that /some/ information has been lost, but very little\n",
"print 'Total deviation:', np.sum(np.abs(data-reconstructed))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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tWqWCggJNmzZNlZWVKi8vDx0zbtw4vfHGG8rKypLH49GXv/xlrVu3znJwxM+G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\n"
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Total deviation: 2.19413017787e-14\n"
]
}
],
"prompt_number": 86
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This allows us to perform operations in the **frequency domain**, like applying a high-pass filter: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set components with low frequencies equal to 0\n",
"resf = res.copy()\n",
"mask = np.abs(fft.fftfreq(data.size)) < 0.25\n",
"resf[mask] = 0\n",
"\n",
"# ifft the modified components\n",
"filtered = fft.ifft(resf)\n",
"\n",
"# And the result is high-pass filtered\n",
"plt.figure(figsize=(15,5))\n",
"plt.plot(data,'k-', lw=3)\n",
"plt.plot(filtered, 'r:', lw=3)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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gwAAlJCRo1apVGj16tFavXh10u2effVZXXXWVjDGhvCROEmrsAJQG1NgBANwipGA3f/58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\nTkSuQmCC\n"
}
],
"prompt_number": 87
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The exact same logic can be applied to 2D images, using the **ftt2** and **ifft2** functions. For instance, let us equalize the amplitude spectrum of an image, so that all frequencies are equally strong."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"from PIL import Image\n",
"import matplotlib.pyplot as plt\n",
"import scipy.fftpack as fft\n",
"\n",
"im = Image.open('python.jpg').convert('L')\n",
"arr = np.array(im, dtype='float')\n",
"res = fft.fft2(arr)\n",
"\n",
"# Just set all amplitudes to one\n",
"new_asp = np.ones(res.shape, dtype='float')\n",
"\n",
"# Then recombine the complex numbers\n",
"real_part = new_asp * np.cos(np.angle(res))\n",
"imag_part = new_asp * np.sin(np.angle(res))\n",
"eq = real_part+(imag_part*1j)\n",
"\n",
"# And do the ifft\n",
"arr_eq = fft.ifft2(eq).real\n",
"\n",
"# Show the result\n",
"# Clear the high frequencies dominate now\n",
"plt.figure()\n",
"plt.imshow(arr_eq, cmap='gray')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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D+3odgXV39OuZFAHTHezdaBoPZPgPd8zYpyNzsktHqNns6S5JfzvC/V5n5kiJ\n4EBrr7wxwXw+n2hZSPNBnuJ4O5GkcEwoHtye9yb65akPbxa9cOFCKDdKlubUpLmi4MSPjo6iaOB9\ni87fUoRhjDiJ4+PjoCmgMzBYaZ5GcW8czMbGhprNZhgM1XyUyavGaRTQbrdDvtVqNd4wykvK+CzF\nHZz54uJifHY8HuvSpUuaTqd66KGHoj+00+lEEPEWJ+m0sMROLLoK/P1R0+lUvV5Ps9lpLyxH78Ej\nYnyTyWlrGU6DTQSMsdVq6fj4OJGtOCIDNbmOsV7OexKEcQCOsDwYE7yQPwbvmQEOGKcpJZvPfSzu\nlMmgeIanyI7U0mgYxwzFgfPGnvicO2nki1zTByqnx05g4v6e9XHhfL2u4EAHucORsga+UYfvpItT\nruMOnNB3b//DrvksY3dK5H6uM3OkTMZ5JucPiaJMzKuafh4oC+1H4TlE98ojEZpUxFEKqVIud9of\nB9ojhfY3MnKvwWAQPB6cqlfvWVhXVEdSoBf4pMPDwwS1QOHNZeWpCQYFRwtCPD4+jj5XnusFN1fU\n6XSqtbW1cEzMxZ0MlXZSPVIk5se9KpWK9vb2VK/Xozi4tLQUbVqHh4fa2dlRLpfT0dFRdELAB7da\nraj+j0ajxDkEoDN0gP5Z/p82UHhdOGA6M5CrpNgJJs2zBD8fE+N0B4dhO9rxlipHgXzHOUhPZ/m/\nF3ykeS8naJUxk4L6PXm2NC/gEmiRm+/McmoBZMfPGAtjyGaz0cBOMEcH/HnSKTft9In3dbvThNbx\nyrgHG0+t3ZnCw3v3iIMjfzMw93Fd97G4rN3Zcw8HLvd6vacj/cM//ENduHBBP/dzPxc/Ozg40FNP\nPaVHH31Un/zkJ9VqteJ3X/jCF/TII4/oscce07e+9a0f/2AjiHEIGBHpLUqBovA9HIenFjgnF6qn\nOM6rOtFP1PK2KklqNpsx13a7HVVAngUnWKlUVCqVEqdOwUV51dBRCigPvqlQOD2QeWVlJTgwRyaz\n2SzR9sOcnGMmqg8GA9Xr9WjtgX/lu46uSPFdadjPns1mAy3ybMZM4KNlLJPJaHd3V71eT+VyWT/6\n0Y9ULBZjJ1W9Xle5XI6uAy8G1Gq1MBDv5YVOoM3LeW6cCuvB3KENSqVSFPagFjCU0Wikw8PDdwVe\n9Mkr7tI82JEdoLc4Jg+OThvAC7KO6U4JLnTCDw8BOOBQnKNMo0+ckwcTHD6Aws9T5bM4xXSW5W+m\n4Jne5kXIvPynAAAgAElEQVSATKf0XgNgDZzzhaYjxXcKxYGO64bzrThsMhZsnfs7J51eIwIzz/O6\nAz6I+zrNcj/XezrSP/iDP9BLL72U+Nnzzz+vp556Sm+88YY+8YlP6Pnnn5ckvfrqq/r617+uV199\nVS+99JI+97nP/ViYjAPJ5XKJXkgESnuMc4xe0USQ/jsWMd3M78rr43EH7WlUvV6P7ZaNRkP1el2l\nUkmNRkOtVitS56Ojo4jsvohseUT5cOJU/OlKwFAwBkcmOH8cPI4EJXUkIc0NHkRcr9ffZSiSQqnY\nObS+vq7xeKz9/f2QF394bclwOAzKwZ0wc8nn89ra2ooAceHCBQ0GA62srKjdbqvdbmtvb0/FYlGN\nRiOQc6fT0f7+vnq9XtAc8JAgPBwKCIusxavpHGjNWJzDu3PnTvwMGfFseEDkA42A84JaAnUT8HF6\nYUQW0NFJr/46H47ugzT9kBXvYgHV8n2cCY7IHY+DBhw2cuIZ2Ihzgzh875BBRwhWDnjcyTui9EDN\nZwkOjN9Rs9NNrCGOkzl4FudFVt9s4Y6Y//Mzz8aQX7pGgfMnUHugdNR6L9d7fvrXfu3XAp1xffOb\n39Szzz4rSXr22Wf1jW98Q5L04osv6rOf/awKhYIefPBBPfzww3r55Zd/7L0dceFEvbjAYjIxFgDF\ndc4EdJEmm0G6UjIN8siNUnrbFIsFh8t2zGazqXa7Hc/ByFB0ojdz8KKNczikld5vOh6Pw8D5nfM4\nLC6IOq1M3vOHUq6urqrT6YSS4TygIvb395XLnfaNUvwAUXOKFPPqdDqxG8hfPXJycqK9vT11Op1w\ngBSOSqWSFhcXA7XjME9OTrS0tCRJ0YZFGokBYwCsAw6SQAlFMx6PY6+5Fyen02liVxhG45zm0dFR\nHErt79PyYuZwOAwnjpwdyXvG4zSVpPi3c3fMge+CmNEl9Nz/eMrqjgwbAEHyeUeWvtEFxO9coyNQ\np8C83dAr3IyHMWO76CPrQ+BlPqB3H2+6oMQYPZPztjd3mG7LHpxYW9cbghwZL+fUIkMpeeSgdxHc\ny/U/4kh3dnZ04cIFSdKFCxe0s7MjSbp161a8D1uStra2dPPmzbveA2PxSO7REeE4t+eoNJPJxK4i\njI1FZ+H4N89wwhrhuoKDjkEPnA8qzV+fPJ1Otbm5Gf/meyiQoz+P8iAefuYtLewUYh95uVyOBXe0\nAI8kJYsaBJB8Ph/HgtVqNZXL5TjQGTTlKBnjoppMMYeT72kBg36ZTCZRPKpWq6F4BDJamNjZ1Gq1\nAgnSN4qh0uBPCkjBDV6TtBKjAW1y/J7Lh+IceuLOzg+6YEcVqB7ZHx8f6/DwMLa2st44GXqNeYbz\ncR7MWCc4fi6vLrNOjpo8awANevrJd/m5/8wRLSd9YQfO+7PW3sWCk8Gh4kC9NuHZkjQvUjmA8UCC\n/Pmb9YZXR+8Jdh7UcJSMxQNIOv33jMz5aqdpfE24N/LD5v1sVA6cATjcz/UTv7PJncWP+/3dru9+\n97sh/EuXLunixYuJCjaL5NU/7ofQcEySEg6MyOMLKs3pBHgjT5G73a4qlUqccoQScm8WHaSCQrVa\nreiXlOaNxP1+P3HUHWiY9F5SNMozr36/r3q9HmQ36STjg590pE4A8lR0MBjolVde0YMPPhh9tIPB\nQMfHx1F4SdMkVPkZt6RAdNeuXdMHP/jBhDz29vbiHAO4TgxseXlZd+7c0fLychhcPn+6tx3Ojf5Q\nqAY3arhS6BPns6EFQE8UkRYWFmIbKuN0DhpDHI9PX7Z4586dQMSs28HBQcI5eWsaTpug5Y7HqQ70\nxmkbnDB6461L7JRj/fgszi6dEsPBYzs4Ai8UutPhuwRHggLBiaDgKbDzsp4dwPtCwXibkn+HjAZ5\n3S3oePeEnzHLPQhYnmUxF57JPHiho2ck2AZyZFyMgTHytyRtb2/r+vXrEUTu5/ofOdILFy5oe3tb\nGxsbun37ttbX1yVJm5ubun79enzuxo0b2tzcvOs9Pv7xj4dDZGsgAiRaoFhM1qNPuhDlLVSSAl3i\nBNNRs1wuhxIfHR3F7iecN2d4egsWi8w9x+NxpLpScssf/6YVh59h7CC48XgcqVetVgunByqA4lhb\nW4sdQ4wDeZTL5UBxpNsPPvhggnfNZrNaWVkJh+w8H/9n7mke6sEHHwyELs0LHL1eL37GWNnKurq6\nGhQERsEaeGW8XC7HmaUYHEcIzman56WCjGezWRw0jYFznCLjck4PZ+IpLFQN78nCGRKk/Mg19MEr\n0JLCieAweY5vAXUnimG6vNMdFC53bID7I29+7zSA6zUOhGczl+l0qkajkaAkCDiOvFgDZEhQdqfH\nOB3xOSJnzqwFWaavB//2YjDz9Pn6NlYcmzttb3NiXTz1dzCVzlrTNNhsNtMDDzygy5cvxzj+/d//\n/T084fz6H6X2zzzzjF544QVJ0gsvvKBPfepT8fOvfe1rGg6Heuutt3T16lV9+MMfvus9cARwdZ6+\npCNvurKMEnjkS6cFTlpzb5AhCpfP59Vut+OzzWYzDKNer8dJ3hQ/lpeXdfv2bWWzWR0cHCibzSY4\nOZCs0wDMDWTocwPJffCDH9S1a9eil5R5gxRwPlRMKRbROrS7uxsKhVIfHh6G8qDk7FZy54JD5P8Y\nDQboGQGBjq1+NLzv7+9rcXFRx8fH0XqE06L9CQNlbjhgXvoGL4nzbDQagTbhZknDx+NxUDDS3BmR\nDfiWXRwkyL3f70fhDmPDAP2NmawV6N8dsfOEnpq6XN0he/FlNpvFtmOez/OQDT8j2HkhkdSW53vB\n1O0BcOKbPBx1Mm4oDpy2653TZtgSXL4XQJ2mwjExVmghnBgykeZcP5QW93XU6rwpY5TmWaIDFrcB\nxpEuDLrf8EAInZfP54NTv5/rPT/92c9+Vh/72Mf0+uuv6/Lly/r7v/97Pffcc/r2t7+tRx99VN/5\nznf03HPPSZKeeOIJfeYzn9ETTzyhp59+Wl/96ld/bGrvgmViXrUnknka44KSlNhpggKkU2yKIiwu\n6RDPx5lNp9PgA1ESbxvJZrPRu0hVHN6NscKpMkbSVmne5O+H5xYKp4d8vPnmm7py5UqCGAcJM0/k\niLNut9vx92OPPRbIeDQaxdZST89Axjg159nc8UgKCgMDYgzIgfUASdEjyutGmOMbb7wRr03BKUJb\n7OzsxDqXy+V4SR5BjgDHvTCoRqORMEgcqCM4tqGCeNJ8dC6XCzTs6I2gx3pCHYBKcZzISJq/MdRR\nOs8B9TgviLNF11lb7zslFWXupN+sETruDt1RnQdi1tSzIs/u6PFFPjghUJ+jSmgPnwc25qd5oYes\nh9/Xi4bODeNEHTE7N0rxzIEWgR15e+qeyWSCOuKefMcRPRQdawICv1+O9MwOdv6TP/mTWCRv/XAO\nFONwQ2Ix00faISwilKf4rmzsxaaFBCNyiJ/P5+P9Sdyf/5PWceJ7NpuNFIgUkWjOM0ej0y2UbFfk\n3E5ev+yLxr3SkZXoSypLettqtcIoaS3KZk9P5d/b23sXpcE7lUiZQcdOS3i1lPVxZAMvBXp85513\ndOHChUib4Z6413A4VLVaDY42XbDhedA7Tl1Mp9PgvhiTV9al5Gs4WG/XAw++yJ9nOjfpuuLozQuK\nnq57O40jJq9C09WQ3uONsaPb2AJICMRPKsr30AW6QBxcePBIZ3nMgX5dArI7b2zL0TmydDk6qnUU\nStbn68j8yHa86OToF3m6o3On7pQegSRdMEPOgB1vRWNtfPysE/rmwE2S/uZv/ib05r2uMz1Gj7+d\n2PXUxkl+hAb0dkV0yI/wpfmrSki32u22ut1u3A8HgpNJR3avalcqlTgLlEowCw5qmM1marVawa9y\nX5rTs9msrl69GkbC3FA2EAbR3VNtScFv8lZS0KejXEcGRGrkAvpfX19XtVqNsz/Zt89YMdhi8fQd\nS91uNxQN9EGafHBwoI2NjThvNZfLqV6v6/DwMOE0CADOL/rGCKgHP3uTy/kuMgB4ZDoJcGSsB8ad\nyWSieIlTYw1pQ0sbkQdBODqM1YtXIBxvdUJ/PJhi2GRQ7gw8eDmYADHxPXcozqF61R2d8h1bnuVJ\nii6JNFXGH2SKvNxGGQNO1WWMDFlb/sYJO2XkOoEduQ0zFuTi2abv/UfPvaaC3uAfnJrinu4fnKOG\na0/r371cZ+ZIPcIhAAQE+vDWFYSJEuRyycOafbsg9ycSdbvdOLGe33EvTgnCGXn136MYqR8nx7OL\nBqNGGTy1dV6yUCjo8PBQFy9eDKVirERsnAP3wqileQ8sCkyVemFhQa1WK2gDGtPffPPNUBKivDdh\nd7tdLS4uxqEjoFzndxnf2tpaOB5HX6PRSCsrK+r3+7p06dK7CgZ8lrF60z1OHdQFNcL3WHNQLmn+\ndHraekXvZyaTiS2mmUwmemFZVzoPJEXTdaFQiAIdXCuvN0H+GDNnCdDRQKqIA3DHy729kR09IhCA\nPB0lSorvObr07xIUcYDOYbNO2A3/97QaJ+ltTE7TeDEnzTUSlLEFP10JB8ozsAkoHKcJcHZOLeBo\nGQ+24eCJMSMn6CoCncuZcbqjdzrBESxrx3p5AdfB3b1cZ+ZIIdDdOThSw6E5ovJ0DkNG4VzJUNJb\nt26pWCxqZWXlXekgjhfOjGiHAOnH9FTLUQM0QS53uhddmqeWoGBSWknRGQAq9pNmXClms1kg7tls\nFkUDdy6M6Td+4zd08+ZNXbx4MRDi7u6uyuWyms1mIJOjoyMNBgMdHR1FmowRcP9ut6tarRbcnPNK\n7gCgEBqNhhYWFkJOnLfKv6vVqnq9XjgxkK5Xb0GwaQebfskZ6AinzO848Qknk0Ypzieydo1GI/TD\nezAxTu/QYO709krS4eFhNO97CxrO1/fDOxpirZmPp73cw3f6SHNnh66TqvIsPsfcQOsgWJAwMieb\n86KOOzj0yh2Jv/LFnTe0j7dAMUbmiNMis2KczBn7d7TqGxWQJem696Eyb5exy92RMz/j87zJlgzB\nO1uwbUfs93KdKSL1KqOUPMcRgaX3OrMoRB3nDr1l5saNG1pZWVGn09Ht27ejCIBC+XNI1zyC+gHD\n3niOQYNo6/V6vHLZ9yOjLKRKnBBfqVQSvBhtS+zuwbF4xd1742azWVAML7/8cjggFHd9fV2dTic4\nTPjS5eXlSGM9TUPRarVaog2JFDCXy6nX68WBLBjj9evXI7jgGPb29qIJP5PJxGubGXe3241TmFhX\nXweoFt4Y6uibrICTtbLZbCBTHIUXG9yB8V3Q7f7+foIDJvg5isLJ49hBuktLS7p+/Xo4KbIhd3YE\nPM+KPOV3fQc0ZDLJRngQMUEHHfQijvPZfJ9n4wi5N3Nio4XzhfT2Ol2GE6KIyc/8955SO2fM2hFg\nuL/PlwCGc+X56BI270Wz9AlNyAc98i4fZJWun+ADcMw8zzMxR7D3ep3pCflMmkk4L+SL4ryXb7fz\nZloW7c0339Te3p7W19dVqVRUqVS0srKitbU1bW9vRzsMqRkOg+2LCNqrj5IShRFHFq+99lo4eVqc\ncJbwvc7zOv+FU+cZbEjwpn1eMIby3bhxI2SDw0MmXoBot9vhFCUlUK6jROZBisQ9cSo3btxIpGyH\nh4caj09fv1IsFrW+vh6IEkcOymZXW6/Xi7MLqB6D+AgeIGFpjuxZZxQcRw7qwnGxViAq5j0ajeJg\nFU8TK5VKOCJ/dQkFC6gZad4zS+ZSKpW0sbERWQjzgWrwtJgxEix8/zuO0sfmaG48HkdB0zlOjJzX\naIOUvSiI/hEIh8NhvJ/Ln4kT4+AYfsfz0Ck4YA/A0rybhO9hq86xImOyQe9HJhClKQan5LzPFL11\n2oRMksDmMkrPAx3GDrEb1p/dhfii+7nOrGr/p3/6p5KUiELeQybNUxj+747OK+2StLy8rFdffVWb\nm5uJtg/u6/yrn+XpCiAlnTsok59hEKAYorzzKY4QPD2u1Wo6PDyM6jXtQDg2RxkYkjfOU6yhMknA\nQQFAtiBUECaKhBKn0yHkw8lN6Yoyu0aQjTQ/9i/NJSNHjAbaI5vNRpGsXC5H36pvz6vVakE9YGBe\nlCIosf58D9kXi0X1+30tLi5GFuAVbMYIBeFUCSeNOWfpiN25eH5HIGRuaaSIXAi8jlSluROBB/bv\ncaWdk/N+jMX7lr2jwKkxP+iF9XbKgPTdC5JeWHPkjk47QnYaAydGhpW2L/8MFIyvo6fgyBfnhk7i\nWJGFB6N0tuUZCp8DbPBz7ovuI+uvfOUrulf3eGaIFI7JFRcESLQkFSNF8jM2QW/ZbFarq6vqdrta\nXV2NSATM98JSPn/6+oq33norcfSfpwUYYbF4+kbM2WwWzcyesmez2eBZ0ikPEZm0FAMDudZqNd26\ndUsPPfRQcIrMd3l5Wa+99lqcdjSZTLSzsxMHpjBO0BuVUmn+RtRcLqednZ1APXyuVqsl0BC9nwQY\nnDbOQVK85gMH5kU65kjDNanvbDZTu92O81UdYXOOQLvdVq1Wi1c5+8EgGDzjoXjkZ8IS7HA+rJkX\nPhYWFuIUrE6nE21vzgW22+1wFjgQjBgng9558QYDREcZA6AAA3eeks942sh909ycc4vpNBMqCASN\nw8QZee81wRqH7foIJw3fjePp9/shT3c0OFHvgXU0Kc1rGf7OJubA+LzY6I7T+U3vjkB3PFBgW/TA\n8gyCJW/cpXe50+nEhhF0qd1uq9PpqNVq6fbt23GObyaTUaPRuC9/dmaI9M/+7M/i345sUHIcD9Vy\nF6xHfjhDKr4oNguEceEsidxwdpysTv8mz81kMpHi+q4kFhvnw4EZ0rxJ2w0Jw6OYw3dppyJo9Pv9\nKOLs7Ozo6OhI6+vrkfavr6/r7bff1qVLl9TtdtVqtWLsPJv2HmlerfZAdHR0FN93Tkw6pRUajYZu\n374dHCY868LCQhwN6KiGsTebTd2+fTuC3MbGRoLEJ/3mtHpeLV0oFOJ8AaryGCdpGVkBcnXHMplM\noiNAUhS4GB9GhzNzQ3W15378nGJcrVZLIEk6FyhweR8w33UKwufgiNcLNwRhnAnrSMoLQkKXnPJC\nN0G0zM0dugMPP1WMflG3NZ7PPYbDYbyCg8850k8jxGKxGNmD96h6X7evAZmW79jCWaeLRoPBILIN\n7J+gzhrzPUfX/nwoCsaC7FlDB3C5XE5f+tKXfvoRqac50rzfjcgF/KeKSjorKeFEqaiyCBioV3Cl\nU64RzkhSnC8qKSLWdDp/jUMmc9q65EUe0nz4FQ768BSf7zofMx6Pw+mx8GyzLBQKcQjx/v6+tre3\nVa1W43Qt0CH77Hk9R7VaTVAKFAVAfI1GI2QwHo/V7XZVrVbVbrcTVVuc42Aw0K1bt8LIQefD4TAQ\nGWfD4gxIGdvttm7duhWvCzk6OkogAwwCWiKXy0Wfab1eD3RCGxm0Bj8fj8c6OjpKcIPoAEWgUqmU\n2JnmKM7flOoHm4Bw3HmPx6eHpTQajQjQcJ0Y8dLSUqLhHePFyTj9g7yk5DmYzi16kPeuBi8IcR+K\nj3wH3cQJSArg4U6cgODord1uh3NF13C+HKpdKBQC2XnW6DUDULxz2+gRz2MO3puay+USRVHGz3cI\nnPwfXSRwplE2upwuGtHNAKXn9km7IPLkM2Rk9+zP7uvT/wsXC8JEpeQrGTBqIp403wnEzh4+B/LC\nIUqnDrrRaCTQI8pAFRPk9thjjyX4QM5hBRWBjtvtttbW1qKi7wo/nU4jTa5UKoGKUTqe6wWJ1157\nTSsrK4FSK5VKbKGkcPPOO++E8zo6Ogqj6HQ6WllZ0fe///0wPpwR/XHeluPFEM4KxUnCHY3HY+3t\n7YUDwYhYH4zSe0Uff/xx9Xq9BGKg+4D7I6+TkxOtra1FZ4SjLpw6qSWOiWIO1MXdKrRkJzhg+kDH\n43GcfoQcQDnci40NGCDz47CUXC4XvbbQQgROHFWz2QwEjs6SMkNFQQWBdBylOkcnKeFk0T/25NOt\nwLwp4MBNovtkZN4pwPOXlpYSa+6Itd1uK5ebH/wDXeMUFk7QU3wvhPr6uK2zXt7XChWAHoCipdPz\nGKDXvAc7m81Gao+8stls9AezwSPNi7LmcPPIk+dDC9zPdaZbRJ3X8eiJE/DIQkpOBHS0ilC5Nw7F\nj+VzpAgyJDp6JN/Z2QmlxCGTisMpMt5CoaD9/f137Yl2SmE4HGplZSWKQd7yQRTGgNfW1qJV6+LF\ni9HzR5HGEW21WtX29rby+dPj6WjOh0IgaqNcXryi+MSOIJwEc67VamFIoLnx+PQgF7Z5kgITvJAJ\nHCeppL++F4fSarW0vLwcRRJQHffp9Xoh56Ojo9j84MGK9fZ0zOXL3BlXmr909MP/4S9LpVIgeIIG\n+7Z5vhdv2LFFECSYgFr5Dmmzp/TI3wtJpNp8zrdDS/OTrUC1PidQNWjNP+sFFe/Pxt4AGBRicTYU\nB71nlDHzXfhJn5d/3rNIXwd0mnsgn1wup1arFQgRmRPgpPmGAx9Pmi/m/nzeC4NOe0DnOLL94he/\n+NOf2gOrfbunV+FQYCnJjXklvNPpJFIcFIu3RhJZKLBI8x1PkPUoFBFxc3MzDi2mTQaOkINKPGXn\n1ctUm0kN+/2+/uu//iuKNU6i4zikU6exu7srSdrd3Q2D3N/fT3Cgb731VuzgKZVKunPnjtbX18OI\nUQbf0858CQhU8qV5n1+9Xo8CG8T8/v6+hsNhVLNBQf62UQIBqZ+/QZVxs76MAbS/tLSk/f394LhI\nt9AF0n3k6x0HoAsPWIwJdA1yd8P14g//B7F71ZfvE6RBkBSkpDmn6MGHQiZIOJ/PxzZhr6qzFs6R\nYtBkS54G+4YMpxFodSJIkAXBfYK6HDxwpY+mw/l6gAKV41D5DPLh7QkET88G6ZQgABCM+YxTdO7k\nvTAMwke+rHUmk4nMR5q38GG/UEjOW7OBolQqxcYYd7isCYED2d3P9RMf7Pw/vcrlcmwbTJPsKK0r\nFY7HlZ6fueNYXl6OlA1UNJvNAh15OgZqKxaLWltbi4Z0CltuZH4vnCUOpVwua2dnJ3b24NAff/zx\nQDnMg3YijOvo6Egf+tCHNBgMdHBwoHw+r83NTX3gAx/QD37wg0CQ9Xo9UFKj0QiukygMCvFiDq/Z\nqFQqunnzplZWVuJcSpCU94hmMpngBuGIDg4OtL6+HsbCBWfMGnY6Ha2urqpSqcTLArmXND9D4Z13\n3tHy8nKsO+hHUnCscHA4M7hKr+CmjUaaH5KMboCamWPauaIXHnT8XFppviVRUsgEHUJ/3BnAwUKd\nIFccpfcN8xz0H2dLJuA8LkgPLhKE65yj009w3Dhdb7ViPUCNOGSCvBdPveuALAwqodvthjPCkTky\nZkyebXpAcA4Um3Ae0ykkaA1JiZZE7Mo7Ppyu8M+gY967zL09I3B++16vM0Ok6QqkGxSRD0eK0DOZ\n5HFjIEo/IuxHP/qROp2O7ty5k1AM0l6P0Lx/Cd6RtgyKKyxiNpsNNOY8HenAcDjU5cuXVS6Xw8mh\nIIVCQXt7ezo6OlKz2dSNGzfi2dVqNXi3Tqejzc1NlctlXb9+XTdu3IgXyHlTNkWVjY2NcFzValUH\nBwc6OjqKzoD0azOYD8EA5HJ0dKR6vR5Ir91uazAY6M6dO5pOp1peXg7ZSIrzQN04i8VinGZ/fHwc\nxRg6HVDU6fT0nfcehKQ5+uFkKrounP9DH7w6i4H460ekeV+qFykxZmnOE4KiyRR8nGzj9dYenC8O\nEh3m8A2Ck1fi+TloFKfsrXbobhrhkRJ73zDFtWq1mqie4+jSHKK3WzE3H7sHek4EQ85e2CLIQMF4\n/7Kn6s4vey1CmnOkOCw4UEAB8yQw4cjJajyDxAc49wuV54VG1g4Zsx5Qapxz65mur9O9Xmf6XnuE\nzQQ8zYGkRzFAcAgilzvtK/VeTtDa1taWtra2Qsloa3Kn1e12I6JiWBD1OEqeQwqMgYAeQD+e+uA8\nibC5XC5eR9ztdrW5uRm9icPhUFeuXIkOgJ2dHW1vb2tlZUWHh4fa3d2NHtNf+IVf0HQ61dWrV7W9\nva2DgwPdvHkz+iNXV1djS+ZsNtMjjzySQEpLS0u6fPmy1tfXtbi4GDufMplMvJ6YvfaLi4vxbnov\nOBUKhcRReJyqNBgMVKvVlMlkgl+l2wADw1F5Zd75Q+9v9UouyMAb3jEevudN4ay3twbxh/S/UDh9\nKwKVaYzKD25xpIJ+gJLIdrwwxRhxrM61UWBDx5m/I0TSaOcdcczITJqjMeYnzc+KRadIr6U5jyjN\nAwH6izx5Pv/2XlwcGEHY6TUQJAfgeJ3CMyXW0TlIxjYajaLrhjXEuRNYkBGBwHlPLtCqO07W3HfT\nMSeoIuoEyJKA7EWye7nOzJF6yk6E4udOzGNAHFzgEY4XvIECjo6OgkOh4gvSIuUA9tMyQVRybsdf\n9zEajbSxsZGI+tJ8USki7ezsaDqdRvXbewmr1Wos3ptvvhnVbJT/5s2bYdRra2sqFk/POwV1lMtl\n3bhxQ/1+X1tbW7py5UoCJYFmvFL+7W9/OyIzweGNN97Qa6+9lihmgAxoAeN1J8z15ORECwsLgT5o\nqyoWi+E8QVFenScoTSaToFW8ckrK5wUWxuWoAUTkBT2+g64wBncKnvaOx/OeX07IX1paStACmUwm\ntmTi1NFDHBn8NBwjaBuHSDAmEDgX7obq3D8FSneepOjsBmM+nqIyL5y6t+6kOx68GAPoAFX7Z1gD\n3w2FjJ3b9y2ncI28kQGgAffNd7zAxv2gHqT5ucFwz9A22Kw7U+dUkac7R56BnHljg/scAiHBkJ/z\ns7Sjfq/rTPtISQvccKQ5bCcd5fM4M4TC93d2dqJHEV4Mg/H0CeWhTcUJ6IWFBR0fH6vT6ejChQvh\nxCmmFAoFHRwcRBM23200Gup2u2o0GqHwd+7c0f7+vvL5vK5du5YoNpAeYfiFwun5oDj3S5cuaTKZ\nRN4ygyMAACAASURBVJcADpRTh0BNOCaUE4O7c+eOer1enLj/wAMPaG9vT9LpW11XV1e1tbUVygV6\nQna3bt0Kh4CTBSWCXKV5oYCCnzSP5pKi44Dx0ULlf6RkCur9kwQjPgsycUSCXH0nD/rDWnuRgx5c\nnDjtV6Asntfv97W6uhrPxNEQGPb39wPNM3bmQ7GQcXg7Eh0YpKCSop/WHQn39HXGedN/zGfcXhwx\nEySwG+a1tLQUjoMgzEEmoGdH22kkjqz5DsGN1237/b3QAxWGPbK+9CojZ/RpMBhoeXlZs9ksTkwj\nODivzPfREQKvpGiF89YmnDi0E/6HbJf7/J/pI/Uqpjf4uuGQGkrz3lL/U6lUtLe3F2+rlJRQQDhM\n+DYQC9GWrXA4hZ//+Z9Xo9GIlJq02s+s7PV6sa2Mc05pz+GEp1qtFu09DzzwQOw3J5Ug4hYKBTUa\njXA+pOwYwOOPPx5tUCsrK6rX63rjjTcCEVD99nSpVqtF61A+n9ft27e1ubmphYUFHRwcaHFxUVev\nXo0eV6qybK9tNBo6PDyMrZOggtFopNXV1aBIOLOA6jGpqSM0CH1HDTjKNOrFeTtXRSDDoFm/NBWA\n/oBMPU3FWDB21wUKJI7AZrPT/koCDShZmhc64Z+9ZQdHwA4uxuApKnM5ODgIx+L8PnKirYwsA16U\nwMa6uZP03XsU6nDWnqWdnJzo+Pg4uPDB4PTtsryLKZvNJigv787AwbGO2Jt3UNy8eTNBjfFs6hPe\n/+t1BOZCoKb7plAohE4DtkDY3AduFZmREeHUnXNGv+r1eqLwyDM9W7qf60wRKYJG6YkSbhwI2BfO\nU0ScMUfEwdP1er3YekgKKs2PwiNa4ZBbrZZeeeWV4LkajUa8+4gox0Lyc6I4RkXVH/SysbGhyeT0\nwBLS8Ycffjjog4ceeihOvR8MBmo0GlpcXNR//Md/qFwua3t7Wy+++KKefPLJSD0feOABTadTbW9v\nx8n9yGQ4HIbj8xN74ACRMU4LzhAeOJfLqVaraXV1VQsLC9rf31e1WlW321Uul9MPf/jD4HJRNpC4\nF+ZAes5hst2WdJTnS0qkWS5TSTEPnuHtUl4QYH286wMeHWMDdfvnoQyQH07BK7fpghG6h9zccTjX\nx1ygp1gP3lFFduLIiqo7eueByyvwODPGgq3gZNJ8Mk6RrKZcLsfa49Ck+SvLJQWYYP3duSBTHD9B\npF6vB/LHcXsQg7LwGgmBmlbD6XSaeBUOektg8jSfsQJGkJfbAltXcaLp9i+CIetM18n9XGe+116a\nbzl0DpP0BONEWfzzNMu7A8YYUWZ2PnESvEdC/iDw9fV1HR4exmK7s/aUc2VlRTdv3gzjBBVzoDE9\nhKQ2fuSZV995gyZN3Bjc2tqadnZ2whmAmkhPSqWS9vb2VK1WY+6k/MiJKEsgcs6HyMv8SPuYD7zm\n2tqaDg8Plc1mtba2Fq9LkU7Ttr29PdVqtUBgcMSgR5xIpVLR0dFRbHTwtFSaGy+cGXPwdhm/fH44\nEOdKHa1Q8fV0dm9vL9CKc8Ve+MSZ+/ui4Fmp6NO3i24Mh0MtLy/Henv6SS+sF6EYvxfc+D/Gz7jc\nOdPG5VQH80a/vK3NC7l3o1T4mf+bNfatsF6M8i2xyBCH6DvW/BnMnazFdZMxu14go+FwGFkCHR8U\nl3yNWBMp6VixHwdnZGK+Towb7vd9Pf3p+vXr+s3f/E39zM/8jH72Z39WX/nKVySd9hc+9dRTevTR\nR/XJT34ycZrSF77wBT3yyCN67LHH9K1vfeuu9yUlcUE6jwHn6QuOQbjCg1juRhQThSDGXVm9aAWK\nYo846QHpL7wWxQqakaVTIpvXbORyueBZWTCOhvOX4vFc9sXTsJ/mBdnnjUIQRUn14AlRQDc2/s13\nmaejPeQGjyTNq+PsQALB7+3tBfqYzWa6ffu21tfXw3hJFTGIQqEQWxBBJU7P8BzQKmPwgpIXKpw/\n9D5BDJMgAAJGbqAw7gnqoaBDHyzojecSxPxoPzYaIMelpSUdHR3p6OgocQiLB0UKmDgl13+CtFMR\nXiBkfr52OGlvA3NH7ogYesLRP3QBz3QUi33wOZy570CDQ+XCPj3jw7486MLjexHQ1xAb904Y5o0O\n+XZXZEewpVsCTp9Un7FxCpavH/+WTp024/PAe6/XezrSQqGgL37xi/rhD3+of/u3f9Pf/d3f6bXX\nXtPzzz+vp556Sm+88YY+8YlP6Pnnn5ckvfrqq/r617+uV199VS+99JI+97nP3bWVgGgGakOwFFBY\n/HRaz+dwjKAPhIwiQGyvrq7qAx/4QBgfNADG4ujGBY1RMA6Mgybx2WymlZUVXbhwQbdv3w5DgmKg\nyEGqg7LjTNnRRM8kaZ00P2aMseIcKUhxb5yCt4ukq5D829GJOymUUJqfiuO9jncrBB4eHkYhIP2n\nWCzq6OhIkqLI5akjCJFUHydGoYD5e2UWxEZGwbxwAjhiAqgbarPZ1GAwUKvVCl1aW1tLNKvfuXMn\n2or4Pi05ZBF8l7GRetMxgtH68YI0fuOkfP3vtu2TIOpGzt+gPoo8nk6DTkHz0ExLS0vhMAkM2BgI\njnsABFh7zwDRAXTDP4MDw6Gig85Fo6Nkef4+LdYRXwCFwVrTt0wgcv4defAc7s8c4X2h77ABDwrM\ng/uPRqPwHfdzvacj3djY0C/+4i9KOt3a9/jjj+vmzZv65je/qWeffVaS9Oyzz+ob3/iGJOnFF1/U\nZz/7WRUKBT344IN6+OGH9fLLL7/rvpDACBDnw0EGRCiM3YtOREX/Pak+SpzP57W0tKR+v6+rV68G\n8iHaUAFEuX2vLUiXyEua7n2ZxeLpgSlXr17V6upqGBPpGIbTarUSlVsPDozfd3Pg7NILzt8YBM5I\nUqI3jvujzB7xkQv3ZzworbeMYJyemnHsH1tVcRqj0Sjei4Vx0PZFQIKzY+eTpESDN+vmY5EUMmDs\nXJ6OIysvNvFzApWnqPBtyG15eTmxFTKTyUQw4NQwdNM5XF6JwqE4xWIxXjuDIyUAoL8EWueluS/F\nTWl+yj+XI2zGjs0gQ5yo2wRIFblgX2xyAOGlC0GMPc2h+t9U49EBxsZaEfzd5kmz3TFjTwQTtjFn\ns9nYbIFtedGY+ZDFODoGiRMUGR9crW9hxWYAI8jifq77KjZdu3ZN3//+9/WRj3xEOzs7cdTbhQsX\ntLOzI0m6deuWtra24jtbW1vxygm/WJx0AQEjAMVhlM7/gBCp/PLah+FwqFu3boWQ+fyVK1eUz+e1\nsrKihYUF7e7uRqSknQnUxXNGo9OtibxqwxUYhzYajfTII48EOuACGbFgjvBAVKRN9CWy8KSMjIVm\nfZyLFxc8pXXZuEP09iQn1B3teapGCgXiX1xcjFe0sPe5Wq0qm83GQdHZbDbW3zk2ijYYLQhpcXEx\nsd0WmaPYpOHeyeEOI80relrPvUCS4/E4+mhZZw6BoZsCR4OhOa3EHDKZjFqtVnxWUujO8fFx7Ozi\nKL/ZbKZqtRqyTxcmmRd9t6TyvKWANJ4A6mgSJOldAwTE3d3d+BzBm/HgHEizGUO9Xo/WKu6LzKGT\naMPye41Go9h8ATiBryT4My6n57Ah5IJOEkRdPmygIZjjHL1gSaAiU0WnCKQ4YOwEubvdOyWCvd3P\ndc977Y+Pj/XpT39aX/7yl+PNmFws5I+77va7733ve+EUrly5oosXLyb4ICK3IyIvLiHs8Xis5eXl\n+OzW1lYgJRwY++A5KIOKIC9SW1paCkOgzYKotLGxkagmE6lR5lqtpr29vejjBAnjTNvttprNZgJ1\n829PSzBaZMCi05wvzdE43CkR35EySo9SMVc+70bgPYeMHcVDoe/cuaOtra04jo7AANrnwF2cL61G\nvDCPNcCwKeSRRrEZAMQIV+UFMS+osPaO6ryQAl2AI0POpHlenaayjMPGIEEww+EwUmRJiUNscrmc\ndnd3EwUf1gVDTXNwIEB0F6fjiNk5vQsXLoQzRDbsaPLOE/QHXp97EJhxUuiRp8cUW73DwAtS0B3I\nr1arRYA/OjoKNI9TwzZYQ8bugREE69QRuo5T8/ODCVicG0HnCbUFslOnCJG1H2VJV4Znrd6G9847\n7+jmzZsx1vu57gmRjkYjffrTn9bv//7v61Of+pSkUxS6vb0tSVF4kKTNzU1dv349vnvjxg1tbm6+\n654f/ehH48+VK1ci7brbFjdP+9IV9Uwmo/39fR0cHCiTycTeeZDWeHz6ojZQF4tNg/iVK1diXz4H\nqbhCsw8dhXAuDAXgcGk/Ro9U2gtdOBxJ4dxQJpBGuVzW66+/Hs4NmoBUezab6Uc/+lGi3cUVFeOR\nFP/O5XLhXEBu8MQ4qOXlZd24cUP1ej1QKKfkc2Yr9AnbRHGa3BcUD9Jn7XDqrCFomJ+TsrucUWQv\nkIEEkYVX+5H7cDjU7du3Va/XdfPmTTWbzaBBvFgF+mWjA2MC6fN/f7kcry3xgpIb79HRUYJ3B1lz\nf9pyyB5AwMyL+3iLHs4WfYfOIf2lKFQoFIK68mIMF/NAbqBOSYkX36XbnAg+OFqcFPpFtZt0Pf3u\nMIIhdBI0F21njBNukmzTD5NhXOig7yzzIittijhanL5zuNQYcNpU6sfjsTY2NvQrv/IrevLJJ/Xr\nv/7rP84d3vV6T0c6m830R3/0R3riiScSLUvPPPOMXnjhBUnSCy+8EA72mWee0de+9jUNh0O99dZb\nunr1qj784Q+/675eWCJieJsEE/boCJrCMbGgpVIptjZ6v+BkMgnE4+m2pKgQt9vtQCz8HqfU6/V0\nfHysV199NbGX19Oq7e1tXb9+PYFCUMTRaKStra0E34uRwv8wL1IrUBAnIaHkcIyTySQoFY6vw4kg\nM9/NhZJ4qvLmm29qf39fx8fHarVa0Y1w8eJF3blzR/l8Xg899FC8/qTVagWC4oxIzitwTnl5eVmN\nRiPeyDqbzV/RjBJXKpVIr1kjCjc4fj8KDoPn/6AmDIM1BqlcunQpCl7ValWtVitxbipGhyE5sqEo\nAZpxtO/OgPE595bLnZ6pQGDxogvrRLsUTtXpGs9U0G+Cv/ctEyw4M4CxoVvISJq3/nDQsXeEOG/r\np85L8x1PuVwuEUigy3x3k6Sg2eD7vWsEW4RLxWY9I0JOjBsuFXv0AiOOmUDOZ3DIoGOn58hOvAZD\npuR1CO8pxt7v9XpPR/q9731P//AP/6B/+Zd/0ZNPPqknn3xSL730kp577jl9+9vf1qOPPqrvfOc7\neu655yRJTzzxhD7zmc/oiSee0NNPP62vfvWrd03t09GNKMYieNEBR+vpK1HSoycpx3B4epam84ko\nCN8FSeLscIA4HVBFJpPR7/zO7ySqk06iz2YzPfroo+Go4QP5LgeJ+AaATCajzc3N6MH0QoR0yivv\n7+8rl8vFO+x3d3fjvNI7d+4EOt/c3NTh4aEajUbCsJ588slAXRyXRzq+urqqarWqlZUVNZvNSFFB\nn1Aei4uLcVgxnKajnLW1NQ0GA+3v72s6nWpnZyf6TnGMKysrkRqzg4YGbUfPbiTSvL+y3++HbHm2\nt9q0222Vy2V1Oh31+31du3ZNjz/+uKbTaZyKxV7wbrertbW1qKajQ/7udoIhLVI4I5ANyBbkhEN1\nJ0PQdB7bgxzy4TvoqcvWizEcMEMQnkwmEYgINvCp/l24aDIdSVGPAMXSbO/tVN5RQ6DGEaLDBBXX\naQ8KFO8IYtgd1Jo7Pp7rOkAQw0lKimwT3cBROzpFV6ElKIpBTXkAxVl7Ics3MNzPdWYN+Z///Ocj\npUV4Tgi78/S2J688e8M66I0IgyOAU8WxsTg073Y6nTgqzpupQUYYwvb2thqNRmzxdOJ+YeH0ZH12\nUDEeEABpOjtUstlsvDoBp0IQaDab+sEPfiDp9GzVZrOp4XCoRqMRHK9znTSXuxNdXFzUzZs31Wg0\nIg3O5/Pa3d3V5uamdnZ2oiEdR+PGNxqNVK1Wtbe3F8gHtFMsFnXr1q04a3RzczMcL9wYyooMnG/C\neCQlOhDo5yTAuh7guBgj//+nf/onffSjH9Xly5fDsDudTsK5MQ6MlNSc9JkTrJzPdK6ecYLkQIPS\nqcNiAwJOcX19Xdvb2wkEx7GCoCQ4ahx3tVpNtEpJCn4bCgqEy+/QbX6e7tUtlUra39+PLajYDJ9j\nfIwFh0iwxRGlU/00isQuQfYEJfhs5MbnAENQDGnnTcBAvukio3eGtFot1Wq1CJSs3XQ6jdf2sM6O\nwpExz4ZnB0Uz3/8TL78jDSBdhbeQ5hVOfucI1ItIKDACIBKhSF7BdE6Ve2ez2cS+dNpyiGTse2fh\nQLFQEqRTVC9B1dzDURULxrOq1aoeeughtdvteK1HNpuNA5K3trbCMU2nU7311luxqF4caTabgZI8\nlePkfpSHFiVSPN5ng0MuFouqVquJcwhwWNPpNM47JfD0ej2Vy+Xoz8RQCQjedkUwIy1l3ZEVzh+F\n99QKg/UAWa/Xtbu7q9/+7d+OFxhOp/OTtyj6OOeOE2NcOBAonnTKi6Phbzdo3rxAJ4J3lLzyyivx\nLizmggN2h8V6wyWjx27Y3jLnTigdnNia7ECBV85I86P06KDw9NwzPuTuCJPxEEh9nVkXAjF6SFER\nJJ4uGKbXBZoAlE+mwqtx0GP0aDwexy5C2vDY+lqtVuMNC/gZp7WQfSYz3x2VnpM703u9zvRgZ9Io\nWovSPYQeifx7KBSK79HaeaSlpaVIeYmsTgc4p+P8EeiOQ1+z2aweeOABZbPZREWcirAkPfTQQ0En\n4OhAzbVaTQsLC2o2m9rb29Ph4WG8n2ltbS32s7O4tF8w5n6/r+Xl5cRCE3SkebsV6SeBKJPJaGVl\nJXhXDiOhGXtxcVEXLlyIA6an09Ntsg899JB6vV68Yvng4EBra2uJt2dWq1VVKpUE+vRdUawlu8JY\nM0/BPFXkUGjQrxuyp77tdlvHx8fx8kGKjbQyYQTcB8S/u7sbPFi5XA46A0dAuuxnNhCYnZ/3ogX7\n0J2rdeoA7hV6xlPL7e3txG47ZMR9RqNRtGF5V0K73Q4kzIEjoHxsg+o8z8PxQh2ANB2Bs16MgUAA\nRUMBiM97kMH54ujSrYxekOP7OC46ELB/ab7xhjNwkRH8slNrACt4636/H1uvcez8jU1NJvOzdAka\n6d109+tIzyy1//M///MQmHOY6Wo0XAuL7A6Gah9pNqkEjrPdbkff6NbWVqKH0klsKoZ+Yg68GApW\nq9W0v78fjoQFJA1cXV3VjRs31Gg0ogG9UqkEKh6Px4k3he7v70d7C0rhSMMrmp4m8jdOxIswVFAp\nXDkX6PyvUyTwb9nsfEsq6aCkaFFijCBhT4+cG+MzbqTpFJm5gl4xDu9tdGNHJjdu3NDW1pZOTk5U\nq9Vii6ejvvSZCnwXR4jsZrNZcKuOqNNIbGNjQ0dHR6E3HGLtvajQRGwzpoVOUnSZsIZkT4zXnYsb\nssuVwAnyQi+8TQx99l1L6B3UBQ7MERpoFDt0ft83IKRRM84MWYLimbfXM/gs4/SiMvMD8fqYGC/V\nfpw093C7cOfK+9WQDQ4ZOcKFovdeiHbb/j+R2hPppDkfKc0dGMKhMooBMHFSLG9rymQyYUjwNZPJ\nRKurq9rd3U0cGuLbECWFQWBMoGSvEIIuSKG8h/DatWtR+Ww2m9rZ2dE777yjra2tQKQsaqfTiXNM\nQcOe3pAG53KnR63xWpKLFy9qfX1d9XpdOzs7un79ejiTbrerVqulvb097e3tBVr2yjcn3y8uLgby\nWllZifdIVavVGAMoE+fmTllSQh6FQiF6/Uj/eC5pGA6YosfJyUn0Jo7H4ziUGx0g/SeQvvPOO4nu\nilu3bkVVGfRHyxC9gawVPazoGwULZMQYmLfvgIMzZ20pTOTz+TiIBUPc39+PE8fQY3egfI4xuDyd\nPmKc3rQuzXuL+ZNO8dFLZMx96Syg4AklQQYB2sQOXeel+dZpZFIoFCL74jU0BGbmgeOnLuDFM6c2\nkDtFokxmfmALlxdw6X5wR+pBpFgsRrsjOkz2hG2RmRDIWQ/017Pie73O7OV3vh8aIRMJEK4L3Z0W\naShIlf4zuCE+59vqlpeXow1qaWkp+FcQraeZ/CEKk76MRqNo/nfkCOplO+Te3p7W19eVz+d1eHiY\nKHwQHJgXUfPk5ES7u7vK5/OhOPv7++p0OomCAMYPBwpXyUn/yIKzHAkajrjK5bL29/eDX5QUbUse\n+eEjPf314g/3cv7Si0IULxjT4uJiIEDnkHE6yLper8fhye12W6PRKCgQ3mzKHnqMid1WBGiMEwfr\nO5lwnq1WK3og0QX6NlkD7zIAGVEY42CXZrOpg4ODeLHg3VA/PcQESeY+m83uejiNc6XSPNtiE4dn\nJsgbG/HKM04Zxwva5C0BUFQAEp7DvL22ALhptVrBX5K9UFj0TQAUoLxnlTl5JikpepZPTk4iGPEZ\nQA4XGQBte8wVmeEvkA98ucvHfQ42jONHlvdznRki9SqqV/XgV3ynhDtVSQlFSUdPaS4cIj4KgeH3\nej29+eabajQagQQ52QdERvHLCx2+E4X04saNGzo6OgoOcTwe6/Lly6rX63G03rVr1/T222+r0+lo\nd3dXt27d0ttvvx1H9oGcqL6jvEtLS9rc3Ew4QxAYZ0rCncKbwTWVSqWoYDMHFNCblWezmWq1Wii1\nV48xIKI5RT44NTcQnCvUCsGBNi6/Ly+4A82xRmtra5pMJjo6OgqnePny5eCJe72earVaoiqMzNi5\nAxodDocJHgy9kOaHsuAEGJdnP3CRXmSazWbRwA+qXVpa0vHxcfQE4+RarVYUL+mpBV176w0U1P7+\nfszHU2aCBeuKjEH5cKCewYBAcdae4cBbs6kAFArXSh82aBfEDDWCY0QPPP2mEAS48X5U3/XGfbFx\ngjB/qLanaSSyJopfOHuyIDjcNGhwXtW7hBgLF/NotVqR+dzrdabtT/zbK/HS/Cg351dwXCgDKTF7\nlXGUXETuNFcGyuSz169fV6VS0aOPPqrBYBAGj6EQZV9//XVtbGwEoqUFhjG8/fbbWlpaUq1WS6QG\n0+k09k/DXTmZ7bwc80TpcYLurDCS9O4UFC69tbLVaml1dVW5XE47Ozu6ePFiItXyMTgy5OBh38qX\nHgeIi35JnK6ntnB5fA8H6T2hBEYcycHBgZrNplZXV5XJZHT16tVIz0CJOCTv/aOQw/xxMI6qcAjO\nzxNwPa3j36SUpPlsifSMxLnBxcXFcOhLS0vx3iUcmhu0c/XOY/sFTw+nC91Rq9UCnTMn9JZAyjpA\nbTgChPsm42GtCD7etw2yY2wUxPg39kSKT6GP9UG+yBJuF2CCHTltwWEl3W5XzWYz2gWRGUGJy3nS\ncrmsg4MD1ev1xDmlzttS3PNMoFg8PdKSTp6/+qu/umeO9Mwc6V/8xV9ImheafKF8MXCEpCakBu50\nHLWiQCAfPyXKdxWluVcfA1GbsTpZD0rBUbAoOGgvqHjRgMtTDE9/HKm4cqI8rgzON2IQ3nPpTeEc\neQeSRr6eyrEOJycngYh83ozf18fTeRw6wYKuA0etTg9giDj0arUaKBSnzX59WrJw0KzJ3t5eFBDc\naSBj5o9TobA0Ho+jLYgdZOgBzt0DNjqEPNCt8XgcCJm2OLhqshsu5uwFQvTYj24jCKJrODrnLnEg\nOJh+vx/jcQfmvCaBV5pz2zh2zzyQLTY1mUxiZxtZn8+L5zpydZTqQQO0KCk2eiBj7uU0xmAwUL1e\njy4U3+3FYT/YHLLl+VCDHpzIftBtgq6U5Ec9gH75y1/+6S82sWMFAYLSEAyCIFp6dS1dHcQASScp\nNKSVhMJKvV4Pstx5HUd6vPZjaWkpWi94NQPtFCzg8fFxICOMjW1zGA/pkFfAUXI4VD/Nh1SdNDK9\nxQ/OzhEYTpq0pFgsxslXzWYzsZNoMBjEbg8QF8Um5IwSwqOxjxrlRlaeutGr6u1CoAxPMVutViD+\ng4MDSfMDtmlPobiEQ+z1erHTiqo9zoixet8vfHMmk4nCpBexMEaM1nco0dvbbDYTfJlzu1tbW4Ey\nafP5wAc+EOtNZuW0QTqw4BhBV+Px6SE8PGs6nZ+fC6qczWaRjgMY0HHsxdG5Oz++A0rGhnCg9H4S\nkHGg6DWyk+bnxjo694Iar+zwg1ZKpVLI1p0r9o1NFgqnr8zmcwRDD9yeOTI2ggB8KwEJB8tneJbz\n0HRE4FPu5zozRPrHf/zHiVYjjMWjiyswyurtGdL8UFamQdQizYBfo6rsaCjdksL9cdxEQk+3cTw4\nMYTurSGMkfv6wRxwtN1uNw568WP4cCyZTCYOyTg4OEi0ZhEg3JFisERpb3BmXKQtjAk0xD2p0nuD\nv0d35Ifc+RwO3CvfjuqohHsqBSIG0Xgv4SOPPKJr165FwEGO2WxWh4eHwe+6AZMqE0iZs/+bz3rQ\nHo1G8aqXfD4fR+Lh2OAOcY4g+NFoFKkjO6VcvzFmH6NnGOiec8d8JpPJBL1Bl4DfmyDG+nvvK/Ji\nTSlGOn0AQnQ6gjk4FeZpubcFeeEUUON64G1KrDnOnYIvQICCFbaLM8ZOCezdbjdOtgLw4EzRS6ci\nsDXGx3zggHmvFDL1ojMB4otf/OJPPyJFeD55EARcHw7BHRSCBKGkSXgq9/Tykcp5Yy/FHf7tBusE\nuFc8WTAis3TqDJyHI7pxpiXOGtKbAtBsNostlvyfBWe8VIBBYDyXz8PBSfP906Bsxk3qDK3Be5dw\nrhySQsrIrhI3qGKxGAUdEAUK6GkozyGQeXEFyoEWJ35GOg76oUvg8PAwnC8Bif7IBx54INJT0AXo\nDzoGasNfskagkeaN3hSIPCCyzRdUDc8JCoIq4tjFbDYbb4JFZ3AgxeLpEYgOEij6IUdPtT2z4jUu\nBB66GdADbCEdULg/QZBgyf050AUb8+o7ci4Wi5HOoxegWKeQHAjxfD+1DN1nDdFHP63eK/ygMcKa\nOwAAIABJREFUYU5T8/s1m80EeIELpYsC1IkNOlr1YqukABHIFvt3rj/NVb/XdWaOFKFISkRc78/0\narVX11goUhCMxA2aVxSw6DhMnBo8DKcr4cgWFhbCGDAm+CRfGDdAxguyoTCAUZKae2+nNOeF2NXk\nykqEZ76gjeXl5UBwJycn8erc6XQaO484CNfRAAc30ElAukZAwzAoqiB/5i3NHTacEwYCx+e0AcFA\nmiOecrkc2/dIqZkbQZFAgWFz8tR0OtXq6qr29vaCLnBagedCj1DIQIdwYpPJRM1mM9Gj6qczYUQU\nxaBpuAdUTKEwf60Mn/GiKbrBXnCcLhkJbV04AcaGU+GeDiJYV+gp3+YpKaggd7TuQHK5XJyvir0w\nXq+O+2cajUboLPPGgbuuAm784CGcPPdlveCeAS3e1cDY4TQJ+PgIaILFxcUojDEXghIBHZQOukWn\n+Yz/TJojea9p3Ot1Zo5UUiAXoiWRxlO5NBeKMnPhMPw0HHeqwHtJsVAgN5Ce9P+oe5Mfuc7r/P+p\nuefu6ql6ICnSpBVZdhxr4SnxxpHpLIII9kaAEBjcJAggZJEAWcT/gK0ggBEjgHcOIGQje2ULQWAo\nRhwEtiMQGZwBcmDJJkX23NXVNXT1UONvUfmc+9wyZZNf5IdGLkBIbFbfuvd9z/Cc5wyv4liMVquV\nKqOgFIReXgQD4SD0K5VKMboNoYSH4V4IGz/D47pBwqiQRMBIIkie8abmLpvNxvBqOmzcCPP+IM9K\npZIKu1h3yklAXdwL1ElohqI3Go0QZu4PLXJxcRHGAkN4cXGh3d3deDYPmbPZrK5evapcLqcHDx6E\nEmEwPv7xj+vo6CgoAhyuo1nQtL87xhqe0flcnhn0DQ0kpROFOEA4t/39/dgPH44xGIzOGGLCFifB\nDgajs6Pghd1YLi0tqdsdtRET7uLI+ANPj5wNBoMY0oEDLBQKEcVICr4XAwWYIGnnXUsYZPac7wKh\nSYroCARJxIATRT993Vg7dAbH5iMTQfLc150Hjgp55Dl7vV7UlrZaLeXzee3t7cX3eURFmRhGFX3g\nMxh+Qn7vtHuS61KHlriQj2e2+S8bwt95SSfHnQ8DLXn5hWfF8dQc3ga6ZJyYH9N7cnISAxh4ln6/\nr2azGQaErK2jSze4CDnPiNEAZZDQ8oQASFtKamal9NntGGuSWpVKJVAa/fQIE+vAujJ/EnRB+Ifg\nSqPwGAEGJWDscSSzs7OB+FFEjDWhFx00hHOExCAL0GCv19P29rYmJye1srISz8nzf/e731WplMyW\ndSTmKGV80AeF8CB3nAo0CXvtfHw2m9XS0tLPRR8Y2w984AOBlODaqEW+fv16KDhHs5BAWVpaCgPM\nunNyJaMIkUkP9Yk8Op1OhOVQV+yP7ykVFSAxDKLnALwqBqqrVquFnHqUh+Hx86uy2axqtVrqGBzk\nlpIjzzd46RJyx3OC6r3ihMjEqwUcYaL3yNzCwoL6/dHwbUkRbfhxOEQJ7D9OAGOP/gDKnuS6VEOK\nsMNZ4HUcbo9fDt3HM5YgThbH68y8uwGvzCYgWM7bgUIofG80GqGs5XI5SrH4fsIab0eEWkBoQdOg\nZUnxHHCHfB8bDWqkjpL1IlHC74BcMYgox2CQTHJCMUBXHCPc7/cDkfvaQ2+4YrvTI3TFuSGwLviN\nRiNQFoMwpqamwgERFTAT1kt45ufng5K5efNmyrBBLbDmhOEgG/aRdQHJeXaX9/KQHMPP8dOErrSK\nXlxcaGtrS9lsNupcqWRot9t655134rmo5vDSON7BS/mgm1hz3sH7zelkg6IBVXqiREo6mJBBr0jx\nChhPiDJzlG481sPrQjF23vYLAKBJhHXN5/OpCgMvTcO4euUNLaqsEd+LsfSORJ4ZI+w0IBQc8kGy\nz/cbMMRzO0eNfqAHT3JdmiEldPf6LynhDfFyZBxZKH7XfwdojjefnJyM4m1Kmfh8v9+PEWh4NCeW\n8/l8eEMMJcmh8YQB3ttLRPjO8dIuUAEJKBc2lOH8/Dx10JeUZKx3d3dTBd0YJCf9uR9okXDWs7aE\nMmS+MVxkkHEsKAYJLZyOF2pDQzCBHc4ZFE5CZ3V1NZSb9YTHxODcv39fs7OzgcwmJyejBGo4HOrg\n4CA+j1KDsjOZjFZWVsLwYTwdXbAuMzMzKhaLURbFxRSvarUa4TpGEI6wUChEJQUOYjgcpmYAkFn2\n1kh4Z/jQwWDUKCEp6oEJe5vNZsgDBtiTnvzM9YUjTKTkvCIGqzAJCoeD8ZGSdlAiANA6/x2vNYZ6\nQA6d+nDuFOMGuKB8zo0gcsS60q7qydtqtRo6NDs7mwrfSYyOc5wY6kxmNP/CqSveJ5/Px7EwHuGy\nnnznk1yXypESmnr218MbOA43nJ6hJHTxsBWhRmh6vZ729/dT3hnBQ9mc93GP9ihec7ykg+8jeQXP\ng0EibEAZQQTUMfrAiFwuF6VZXtaRz+e1vLwcaNwHfyB09LBjZMkYk+gZDAaBqsmKU0nQ7/dTCIIQ\nCqWDS0OJWAdH7zgwuDvCdRJI8GtulKTR8JHFxUVNT0/r+Pg40A9co4eSRBoIPj/DUGWzSXG+F2Dj\nwEhyuFOQFAMznCvESTmnub+/H/JKDSmnBFB37GdjIY+sIfck08+7YEz7/X44LkJc9h8ZZy3YY4wU\nTpL9wJGjOzhMIiMMyMnJSeQM3ImCtFnfk5OToBU874DBIRr0CV5ECrOzsxFxggqJmLxqhL3H8cOH\ngzSh50qlkhYWFmLPiOJ4DnRqbW0tZTyRr0wmExQA+ikp1hh++UmuSzOkLJqH5SwoXh4kAfLD63Ks\ngnsNDx2cU83lclpaWop7YQBIqkhKZeS3tra0uroa3CS1fIQHhBR4Q46jRQjxctzXCXa6hjysxLiM\nh/0IEKE0zoH7YpzPz891fHycatljHfksDoGhHz7hCW/va8z3EFaxJ+NhsTRyht7vTyYXx0UyiZpT\nwi3WBYdFeFooFLS4uKi5ubkIneEcuRxJo/AgbtYRA8CzYpQxQr4+hKlMC+t0OoGGaQXO5XJ65pln\ndH5+rpOTE+3u7qparWpnZyccP6couKFzugiQANfLv2OcXL48i354eKjT09MwVo1GI9YMwwKggI7y\niAiHjUPkc0dHRyHfklIZf2QQPVtaWoryK6fgPCogMnT05zwmn8NYerIJXQEs+KGAOBP2HcTuGXgq\nUQBJ44OCeD9oJvTWk5NETUR9T3JdWkE+B+m5Z+Xvbgy9Q8RDHoSHkBTF4nW8zIJjeWk7dP6SjWdz\n3UMhHBMTE6kz0L3MAgOA0IF+isVk4jy/1263NT8/HyExG4tAEbJ4JwZe3aeF84fieQwqguOhH2VH\n/C6Ik64e7osB9UQDSk4SA+cnJcdslMvlMGKsN4aY5+SiCoB79Pv9qHoA2VMexR/QJQrsFQisvX8O\nuUGZkR2MKy2hLl84G0+0YUx4Hw4CJLEBusfwsY6eCPNkJhlz1nNubk67u7sqFovRV856Ee1w0QPO\nM8Gvzs/PBwJkfXBiXnWBg242m1pcXEzJCYlW5AGHKCnCcp6HKIDfJYrzn7kc5fP5kGcpaTEmKYjB\nI3IB6BARMHRnfLyl02fICc+PfAyHw1QuxEEZIKDT6cSzAEyQ01wup6985SuPnXS6NEQKUoErBPZ7\n6IHhlJIOJnguLxPi90AC8Jf5fD7G3iHAfBYCG0+P0YBsxlMNBoPgmcanHZEtJTQilMZYUgmAktNu\niOH0ek5CMrLbbLLX1XIhLOMoBuOBYSE8IoTknTzcQfFBXvBcjvDm5uaCB/XQx7vFML5UO7hCYVTg\nwfj89PS0VlZWtLKyolKpFA4PY8rzekTA5c/ohp93yOfz0V6KvPnMS5Su3W7HeuJs4ZDz+bw2NjZU\nKBRSJ7dub28HigLBchQM9BAOBOflDSIUvM/NzYWsHB0d6ezsLJASdbXtdlu1Wi0VIiOLW1tbYTzZ\nG4xopzMacl2tVqOOeXFxMYadw0UTqbBunvgkVKeczsvVeBcpOSoGbhw5QGcBRqBnnD7T0tgvaBUi\nCPQRG4DhxOmgy1CEOB9qw73aJp9POsQ8Wd1oNKLEETrIncnjXr/QkJ6fn+vjH/+4PvKRj+jZZ5/V\nF7/4RUlSrVbT7du39fTTT+uzn/1sEOeS9OUvf1nvf//79cwzz+iNN9547y/+n0UAKbHwjmAQGLwI\nnoWwkA1mobgn9yGBQzjtaKtYLMbAZO/EwTA58uF5j4+PA/ZjJJiTmc1mo/+bf8dQ4fX5DpALHUMI\nPqiC3yfUwQOzwYQ9lPogYHhrBNgNDWjRHQHf5aExwogygJIHg1HX0czMjI6Pj1PCzbqT1eVYYhwb\nF9ydlPTxkxQhCQNKpcON9yASASGSCON94aOlEQqHd+Z7uRcOFKX3iAjnBmc5HA51dHQUmWTQKNys\nh9mVSiX610HX3NufjftSH8v6zM7OpqpHMLyuE9AKOFrW2Gt/2ROcXaVSCePGwY1HR0fa2NiISAPU\nxvOgiwyxQf7GnZnTSSBMnAiAAZSXySRHt3g4zvt75ADVwX1wWKB3gALIFeqI9We9MIY4ZCI1ZIkK\nHk4nRb/HgcvjXL/QkE5MTOh73/uefvSjH+k//uM/9L3vfU/f//739corr+j27dv6yU9+oueff16v\nvPKKJOmtt97SN77xDb311lv6zne+o5dffvk9H8ghNi//qNAUL4KX5N8chcIJksllU/gvHgwjSM85\nZHq/34+uJM9Kw61giDzMOTo6iuJ7QlkK0HkmDDwCAtrDCIBqpWTiFZvuHCZlXggF/dNcvCNK4bwQ\nxodEFmsK7+TcHU4KQeJ+dCSxxpubm/FcUsJv+97hPDDUCC+GhX2q1+uR6SazPD09HZyoO1LeE1TC\nc3s0wWe8BhanQTjMZ1l3nBNo5PT0NLLycHck6zjSmcTW5uZmRBYgq0ajEYfa9Xo91Wq1WH/msJJM\nY0+XlpaCagJlgjBBySRWDg4OAt15ORHOCENL6Rjr45n1arUa3V8MBcGYYqApt2P9+F0vrXMQNDEx\nEcOWeQbkmv8i786zezE8/D1IkX/HqSMXLgs4e9YLg43ssDdOTyC7OH+cnlf1PMn1S0N7Qj5CsnK5\nrNdff1137tyRJN25c0ff+ta3JEnf/va39dJLL6lQKOj69eu6deuW7t69+8j78vIsqqRIhIy/tNcl\njmeKWSgEZnFxMbLH5XJZrVZLrVYrEBkZcBSZ7ybx5QNdneRuNptaWVnR3NxcJE0IpwghGc8GWkBw\nMplMJJrg40AWCCOK02w24ztRBhAU7zwYDGLyvqRIxnjm1on08bY9TxggfBjDfD4fHTjNZjOmleMM\nMCg+GBj0ynqBJODnMPJQOYXC6OiQTqejvb09NRqNCFMzmVFhOMOHvfYyk8moXC6nusFKpVJk7FFW\nOE2MMhEGCuN93OyzI6VSaXQOPOj32rVr8Z65XE4HBwfBd/70pz+NmlgUkQgFBEeGGOXE4bJ/1WpV\nW1tbgfSbzaYKhUKU72C42CvvYPPEGdQOFBHI3TlrjDQhOi3ROIt6va579+5FaO2RFfdjHzFU8JwY\nST7vg3ZA8AAc9NurYjwxKCVtqxhGqjmQBU9GSUmUy31qtVoYZt7bcxusGREdZY+sy5Ncv9SQDgYD\nfeQjH1GlUtGnP/1pffCDH9T+/n5wRpVKRfv7+5JGpSxXrlyJ371y5Yq2t7cfeV8Pa0ApGBv3xmwe\noRGKyOaD8AaDQQj/xUVyDMH09HTAfg+jx1vBEDiUwRMzw+GojpSEERuPEccgsV6Ua2BEnK6gG8jR\nhHtVhBMDwhBpJ+8dSUoKDhZnR5gvKUJNBAyj5FlfkIA0Cuk5VRSkw57gsIbDYRSI876uMDgGZnmy\nP+481tfXw2BlMpngRzH0HHHCOeugE5J0oC2MBpdTDHwX+wpXOM614gR8v5h63+1242ws+Ot33nkn\n5GllZSVkZJwTpEIBxebZoZNIGDLicGVlJcL+i4sLfeADH4jSnsFgEI5lbm4u0CLAw8ulZmZmIu/g\nBofE7OzsrM7OztRqtdRut8OYSqOGhPX19ZBhaA2ilPGIolAoROLN+Wjk0sN5ZB25R25wWMg0xhZn\n6slc9Ac9xmnznSD1TCajxcXFQP4YUMrweDYcq5eVebL7ca9fakiz2ax+9KMfaWtrS//4j/+o733v\ne6l/H8/Mjl/v9W/f//73dffuXd29e1f3798PA4jXICHkCRkWiIusLMKDQHsdYD6fjzCaRIiHQxgH\nPDKK6PVxCICXBnlYzv34Lng0wnA6jdzIcx8cBZvrCNHRlNcUYmj4rKTgffk7XNzOzk4MW3bjIiXl\nIPBf/G6n0wkBRPgID50z5hRU9oSsOaj7/v8cCAgv6XwcqIIjr09OTjQ7OxtrRIkOw51Bt37UClyl\nt7aCMCj8p7RtnC4iFO50Opqfn4819z3tdDq6evVqtIByz8985jMqlUq6f/9+GGmM2cLCQgwmmZiY\n0OLiYjhNmiSOjo4kKe53fHwcjop5B0RHcJVePwlN4pRKr9eLhBXvSwSCcx0MBtGxtbCwEOExawLS\nRAfJRfR6vYhMMaBETByhDSjCqDHgBEeNUR8MBpqdnQ30h375UBKoG2pw0V2+E2MrJRSh178SVSKr\nDlp4x1KplNKv8/NzHRwc6Ic//KHu3r2rN9988z1t2qOuxy7fn5+f12//9m/rX/7lX1SpVLS3t6e1\ntTXt7u7GXM3NzU09fPgwfmdra0ubm5uPvN9HP/rReFmvJwPN+PlIUjJlnJCMTXkU98LGoPi1Wi0O\nhltcXIywGGSDIvjvSoqyIe7N93iI46UYhEz7+/thPOfm5mKQLcYZoh00DX+GAaIsREqG5/rauMd0\nZEBIy9qcnZ1pfX09HIiXqziaJwRz5NntduPAQEd0hEkYSAYFI9hwVPv7+/rABz6gTmfU3spYNt6l\nUqmoXq+HArMXZNdByaxLrVZTNpvVwcGBlpaWJI1CZJAsIeF4kgLEQcgO6sTpQmV0u12Vy+VQQvaF\nrHexWNT6+rr29vZiEDXhfavVSq0PRzKfnZ0FP8q+UoLlST76xDGiUhJ5kfBBD+h8I/oiQrm4uNDq\n6mqKKhrnufP5vN73vvcFSgTFIt/jZU441kKhENUNJLZw4h7WswbwxDg9vx8hNo7ROXXe2xOkk5OT\nEeqTmESGkE32FKdCgswNMKWP7giQG+Tlxo0b2tzcDFn+p3/6p8c1j78YkVar1cjIn52d6e/+7u/0\n3HPP6YUXXtCrr74qSXr11Vf1uc99TpL0wgsv6LXXXotykLffflsf+9jHHnlvhNv7xKWk99wXAnTn\nGWw8kfOjGEIWlvtR/4exW1lZSRkgPCCCQ8lUt9uNVlP37l7z9vDhwxAwPDeoERSDMILWPDnhFQkY\nKX9/Np11GAwGEWJKSnlneCl4Nvrg+Z7hcBhomJCNMhG+hwwvIReHzvHeXHznxsZGGEK+p1qtam1t\nLRA0RtkrIhYXFyOEAwWBQo6Pj1PcGOtCQgPUxbPzh3CXJCLPSEhMxIOy49hQXq9/9KREsViMASWg\nXxKWyBxKiZFjhgEJGKdVvDSNtcbQTE9Pq9FoRHSEoRgMRs0fNGaA2GdnZ/XgwQMVCqPJZV6RMDs7\nq2azmUKrXrUipYdm46wxYjgTQEOpVNLx8bH29/dTkSicPp+7uBgdIY7jRv5J8HheASBEJQoyg6OD\nivGqGvYIOsqjJknBmSMngAqMLLbCh3ZLSnHRT1r+9AsR6e7uru7cuRNf/IUvfEHPP/+8nnvuOb34\n4ov6+te/ruvXr+ub3/ymJOnZZ5/Viy++qGeffVb5fF5f+9rX3jO0J2mEYDlX59wHi+Ntk2yiZ/TH\nDQv8B8rKRTYRHmq8JAI0RKiNAXTPKilQsR/zXCgUImFGsoZuGYw1ntTb7Cg+9sGzCBnPye8+ysg6\nrwQ64j3hW8mKc4/FxUUdHh6GMIIsQDMIOMkrEgWsIUaCWkeMTqFQiCjE6QBqdukyOzg4iDVwrhge\n02sX+S/77yViOA83aNIIvbRaraibpEbQ0SvGSEoK89lvjwwwuiSBdnZ2woF4go335FlAgpRzYcTZ\nNwa2wGeSoKRpwIfPgPgwrHCrtELCJyM3g8EgRRN4KZ7X5fpxKyDJ+fn5WF+MOevGEGunX/w7+bxP\n28KxNJvN6Loan/jGpK9sNpuq2MBBDofDGMjd7Xa1s7OjhYUFXVxcRMcVNAOUDWAKp4B+wrUim56E\nRb5+EV35qOtSD7/DYHmoCg9Ctk1KSiXwcD4ajN8HtSEAXs+GUkiKzziXl8uNivVJcJBxRRD8+6Vk\nBKCk6Jn2LC0C5plhFIlwhKLohYUFNZvNaEccr2TgeT0h4s4GxXe+lxIbR6sYPxwCRoJWRdYWY+J0\ngt+PhAPGrlqtanV1NRXSsg7sDfvgwz0ajUbwdHToYPQxNDgMqB5PfFHcT1jr/K9nZb0sjCJ1T4C4\nMYWjRl58HYrF0XhCpkM5BwlV4o4W40K3Fo4W/pI9Zi2RLTfo3W43DgYERfLOcIG098IVY3D39/dj\n1ilOiu/kvbyJBWfvGXr2kPdpNBpxHDbrwjrSQOIZdCmphsEhUlIGnUQime+B4mG/MbouU8gI706U\nenJyEjrLM7lj5B15buSez7ttkfR/46gRMrogHRII/BeFIHx3CI7Hwih4+EddHJ4blAMaGUdwznNi\nKDA43i3hC0oyw4cMQ6iDykAYvBv3IsN8fn4elMPc3JxKpVLwcRgLUByEP8YC1IVA8GwevkgjZMt9\nMLSSQoiZ1A53R6aZTD8CRVjmnHU2m9W9e/cCMaMcGD738CibD01eWlqKPTg4OFC73Q4Exe97UtBL\nWzDu48dFO4/u3W1EJxi2er0etIsPfOZ9CRtxZkQ/IJxarRZyA7J1rg5kReRDAgWuFWPG5UlD3gv+\nsdVqRf/8xcVFZJ1p62SiFuuAcWCuLlEJMsJzIk8eGWGoqHfl93u9nqrVaiBekKfnKDKZTOQCJKXK\nmjyB5VEOjsenerGGjioBDeQi0Ftk3jlfjLxTgEQVVByQ5MQBAabcKD/pdanTnxB4NhKDx8Lh9brd\nbpx46ckdL29hg8kCs0CeZYdwzudHA5gZrwfXBMrEYOGp4a8ajYZqtZq63W4YAgSE72CjpcTgj/M9\nUjIxn03HoFLegXB5aA2C8NIRD0s8c4lRdEF3ro01J/HhFAdox50LRoCr1+vp+vXrsTZ8N0YB4895\nRzgHnhsuGaTLWsDtErJzzhN/531yuVzwhRhQ9oDn5Xmc+xwOh4GqvDWSsN0jH0e5zuFfu3Yt2hBx\nFhhTQl7kxofGELbi8DOZTPCWZOSlZBQeM1B57mx21FYJYuNzMzMzQUkxHk5SJKowZBg5R2dQBzh+\njDL3J/pZWlpK5QjgxH1vSLB54T+GkMy4vx8RgDeNeLLPy7ZI/FKeVygUonvMu6+IZnlXKaGCiBZx\ndOgMP2dfPEJ+3OtS55E6XMdzESqxkXAbbizwOnzWkxKQzhg3+DsMaj4/mjdK2DPOOcFhcm8QwMHB\ngTY3N1WpVLS8vKxarfZzAgFq4XlLpVI8N4LrzkFS9DyzHnCvKDDVC/wXQYZPg+LwEBp6AKPoRdms\nS6FQiBZVpi2hWJ7YI0LA4DJV3odNeEhI6Mn7MvCYd5yamtLh4WHUCaIY/lwYNRA8zhFFKxaL0bqJ\nI8Ewk2CgvtJRNE7PGxC83djXDOfjtIo0QvPNZjMMFoYaFM86eZjIniHjThnx3CSw/FnYY3g9ECgF\n+fCPjUYjQAAlPX6mUSaTCeTpiTr2jghuvD4bQAOiwwn6UTasv4f6RFYYb3TAI1D0nt8fL3fiGdEZ\nb/v15Gs2m42KAvSCma5ws6yrgwESqJ4wY6CL9OTnNl06Ih0vqUDx8UzwVRhMD+VRBjLrGGBKXTwc\nZmH6/X5MZQKZIgTUeHqGlfIYSmOgDpaXl2PmJooHJQEqwjBKCWfmWXhCXD6PcM7NzYUR452ZGMQM\n0ZOTkxiKAjri/dzbYtAJq/l3SqVIUDWbTUlJ0onnoQ8eZAjH6Ik+KhE8eYhxd3QPIlteXta9e/fU\n6/Wii6ler6c6g5ABUCoGb3p6Wg8ePNDy8nJqxB1h7+TkZCBXQlxvYsCIOnVASCcl5zxJinUAneK4\nnO/jmOBud9TuCiKDd8UIQ5m48ej3+3FII3XTzDIAweF8QG4gwImJiTivC0dH1AaynJmZiYE6lGk5\nR4gcOreO7kiKSAL5ZI3c2bA+rAno2CM6aCMHTJlMJkCO0wXOUzv/7Af7eRkbMsu6ssdw9SSquPh/\nj+r4ObkK1uRJrkszpBga/t9/5p0qbAAeTVLKUyFgzoFJSY8+2UtPAkiJQSHTi0emlIaQs9vtam1t\nLRbXM+90qHh9ZaEwGpyM8DHdx7tZnIshwYQAY5Q8tIGzo4THKxjw3hgub0ogKYLXRdB4ftAFHDSJ\nLN6D0XEYbJAuhsJDPNaH+zPZCB6S5240GpKSjiuMxPLycpwYykAP1grDyH5cvXo13sdRL40QoDQp\nGdyLQ0MmUFaUne4epwZ4TtYQR4mcFovFGIcnKUq8kIVOpxMVA+wBP2ffkKnV1dWgnpBfjDFF+CSl\nAA77+/vRGkx3EaicwnO+10cuuiF1Ppsp/s6Nnp6eRhMLhsurQsYraNAPQA5Gn4Qu+8rFekuKBCgy\nDLiCHoEyYjKW03oeQXS73Wi3ZfgNF8mqcZ1gj/m5V/o8znVphtQ7HqQkIUEZA96bRXYaQEqGfGAg\nvbsEBSO0dtTEIuLd+HcSGMD9D33oQ2EQQQ0oJd8D3+hT6Pf29gI5Ekp51tTDGu9XJ0TBECF8KC7r\nQxiE4jtKWVpaCmSCsnMECO/MfXgexgj6ZChQIENZUDa8NXWqcII4M1ALa1Or1aIUhpCdvnMcAzxn\nLpfT6uqqisVi9OGzvww1kZJuNt6PfaYCod/vq16vB1prtVpaWVkJzpmTXt1pYDj4gzHOnIwMAAAg\nAElEQVSSFPLW7XZ1dHQUDQt0ikmjtkqoF/aCvRoMBsGDYkhIktHqmc1m45hp0BU6USqV9Pbbb0dt\nJghvenpak5OTWlhYCH2BEvAkaKFQUL1eD/oDueF5QV60irK+yDiJLqgadJF3xSFlMsnBhtLI+KNb\nnpzF2UMP4eCInKSRcQNdZzKjY2RofuAIGmSDNWW/eG5kBGNLtOKGEgOKvcBpUj/+JNelIlLCJUd0\nXsbjhhbPISnVzomh9WyxZ9qB+njoubm5CFMxYBgEFGl5eVl3797V8vJyHGnA9yGgPCObw983NjYk\njRJNbH6v14suHu+m4J29z50NHveIfNYpDYQCJTk9PVWr1dLCwoLq9XrKsTiC8Ow63CMol3DakTJG\nj2fOZDKBklhHjAYIEYPaarW0uLiodrsdHXCStL6+HkqF4R1XBvb75OQkuG/kw4+TwZCDWjCUhLk4\nFrqsiFBQevYIagh6gQgAZ0i1AwiY70RpQZqOYr0kDrrJ5Yjjvz2hhXHi37zNlGQT9ZX9/ujkzImJ\nCR0cHKTWiftRFSIp1pjqAEJ0nBl/Zz8xZvwhZwA6dJCA84U+wZlICn6XyMfXBIOMQ+/3+zH+Dxlw\n3p/8Ca27rDV7TjkcBrbX60UiD71Ch/jj7wt3/iTXpRnSqamp4JxQainpi2VhpGQQBoqK8fVsM8gI\n5ZJGwtJsNsOgsjGbm5s6ODiITWMTC4VCeHi4mfPzcy0tLaUyfM6zgGoJg3l2FMWfne/n3xAor1rg\nO/gvIYs3D3g5C+ElISpGc2FhIY6c5ll5HowWjufk5CQm3SPoR0dHqTXneUFW7XY7Cu/HOVG+p1Kp\nRHeVH90sKSiFmZkZNRqNqBUsFApaW1uL6gcSguVyOZVgu3nzZmSYWZtarRaKQGKBPaJAHFmAj5MU\nDoW/E24SKqPs/JwJYxMTE5F8Yw0IRX192TOSJX5vIiFkBsRYKpVUqVRSyBvDOz09HQkW0B6ywLEv\nPuiFZBbIFYNzdHQUxov3dyOP0QVgELVgQJl2lsvlYmoZeszAc9Aw38vsieFwGHWnrA0VH8j74eFh\n6AigR0qcAfMZsAVeygh/LiXnVVWr1bAv2Byn93A83vzxuNelGVJa6Fg4v1CYiYmJVKkMgowgOiJh\ncz3rXCgUol4MD3h+fq56vR7dIKBg+C9QFouNoEOm+4YxIQh0QKjuI7gY3uChgvMy8JkIFhuNYEkK\nQ+RJMwwmGWQQKt56MBhEGOvHVKD0CBvJJJAnxm5jYyPl6d3pwW0eHh6Go/BxbBMTEyqXyzo6Ogol\nAbnivDiAj2HRJKt6vZ52dnYCAZJJ3t3dTSGt7e3t4I4xmGSzQZyONrygH6VkvTBi3nJIxltKDCuo\nXkrqJOmqQW7HExxQQhwPjNHn5/l8Prps2E+MJ+/h2XQGu6yvr0cZFHs0MzMT5VeefNzb2wse1Okp\nqjZ4P0qYcLRMeGONC4VCJEJbrZaq1aokBR1CAg19Qv8wdNBVGEyoL0nxjtPT03GK6/r6esg5yTCi\nH1CvlBwC6Z1pRCHsH3sAVQRA4ZmxKdRd87uPe11q+ZMjOowiiMnr3kAeoAqEhN/D+GHogPbO2eHV\n2QhvPTs4OAgDKSXDhDEqKB3CQHhWLpcjFHcuzCkGDCXChNHAuDnfinJSKD2OynE8/IwCdpAbRDx8\nWaFQiNBwOBzGUGNmWfJ3EmZe5+pe2/vo2R/v7IKLJkm3urqaqp9EUHEIIG8y3bVaTf1+X7u7u8pm\ns6lWWdAjCQb2tdFoRDMBbbkYCpCeI06oG4xdPj+aoEVbIcqDg/fEBck46mL39vbC4VHJgaFGDh3B\nkjHHUeGUPenjUQeGHDSPsSmVSlpcXFQ+n9fR0ZHm5+djtqk/C4Nk4DwpyfJQngSYJz55Z4wyRh1Z\nZo+hwvyIcoAIIAYahGQaa0FCmAiK33PKCvvge+fJQfIoExMTun//fhhoWoXHKSNsDHoJVZPL5cKh\nU6GCQ8BIP7Y9+3+wgf8rFwICH4hwowSOnDBI8FYouZQMX0bQQbJkOBEWSSmE510iV65cCcXjD4YV\nRIUiEiLB20mJUwAle+bXy2mcY1pYWEgdhpbJZGLKj4+SIxmB5/R2SBAE/w4fjKHHgCFATH3n/ev1\nery3d2EhSBgxH20GH+w8NOE1+0KNLbwrji+Tyaher4eBpywLfvm5556Le/I9OBf2COUrl8tqt9ua\nnJyMZgqMImccSUkJD5UAJycnQSsgL7wf68CxyhhOL8mRFEYGFOsF8352kjtmvod3kpLpYpTJeVfX\nxMREDBxxo0SWfm1tTdVqNQwa+0Y9JPJHJhy5prwPQ8Fae32qpJA3OqowctVqVfl8Xuvr66lOLZAc\na4mxdD11OabWl+9yA81a4hAAUPwOso9NACBRuYBOsmcYWEmxB7z7wsKCVlZWAj1T0sizPe51aYYU\njorF9iwg6HE4HAav4x0SGESEwfvKMRIIEAoOSsNDdbvdCKn4LhABRoLv8xKLiYkJraysxDG2GCvn\nElEAKenQcAoDYZIUBnQ4HOrw8DAcCb+DYDlfSnjGNCBJKdTn1QhSchLrOEWCojka4j0wyCAakChr\nRWLn3XffDZTA9/ihcxhZkA+DOngOSVpbW9Ps7Kx+9rOfhRwg7CiGo0UM28bGRsgRVQnD4VDr6+vB\nYR4fH0dShrUBxbnxp0yNtWCffIISzh6Z8C4Z0Bh0BM4MJyEppg3h9M7Pz2O4Ctl6wk+cmNNE8K6S\nYkTjwsJCOKPNzc3QD2pzLy4uonWTyhBPPhJRYLhAq17tAWIdDocxKIXwGaOGcyLfACeOs3KunqYB\nyqqcOmG/MabYAPaK0YHIFs4H7rrdbkf7LHvChfFF7pBnKArPg/yfCu29BZHwEsWm24KsnId8nmiR\nko4V51MY4AC34kaaDDLeHxKdUp3xTCb3mJiYULvdjs8TWmOcoQA4gdKRDELlnllS3FtS6pRRD0mk\nZCKUpMiqX7t2LcJuhJhyENYYhSS8IyNNeM0eQBk4T4rhQdGlpA5yMBhoZ2dHm5ubIXR0HPEsRBS+\n15JCSQeDgZaWliJsX15eTmVpOZWAxIe3nmLQUXISJeODX2g3PT8/jzF4lA55+EZigy6rxcVFTU1N\naXd3N0JsEig4gm63q93d3ZT8gaowXFAbJAA9U48M+SCQfr8f9ZyEprwn+8lROjhWorrDw8MABlNT\nU1peXtZwmDSneCadnzNb1RO1ZOo7nU5ERtSxonOUYDnNNBwOwyHUajUNh8NAuTgOn7dAuC0lrcqe\n1GJ9yOA7jYKdINknKRweySw4WPaG92JfeBfkHX0jCn4ie/ZEn/5fvBAC0ATKhvHCaHLeErwHRgOP\nKCmVVCCkRNm8f5mFWlxcjHFzGEg8os8r9OqBcrkcgghCRGl5fknhQSVF4gvjziZRl+cthiAJUA/C\n70ae5wI90KbovJw/P/c7ODiIkJPyHY4m5vc82yolYwKdWsHIMzNzaWkpFSF0u6O5oygV753P58Nw\ngmBWVlbCiR0cHOjk5ETNZjOSXxwlLCXJNpIeyI8b01KpFEdo8D7sC883HrUQ5vKexWIxFJ1kypUr\nVyI5sbi4GEeBUwcKz+zoyUtpJAUYQH6hdSRF2A3iGg6HcbyM7wPJE5wG6BJEjqMim08yFNkBdToC\nJMqBLsOwgfQABNA+PIfvLQ6Mz0BdeZ0wlIPPXZCS4dWACZy3z5gAvXa73ZBtD+19chY1r0tLS7H/\nOBCoQvTak0z8P5UUvnePe12aIQXy+6YiAGweoaCUJFt8biZHOnjpgocIhBkoCfV/bJAvrIcfeE8E\nAENAeI6iSIrFx6Mx/zKfHx2y50MYut3R8b1OhPNnvHzIwx2MK/fg3bkIITEQJI3goRmwwrtxT8pr\n+Fkul/TQY6BI2HBUClzUyspKrDP35r6sk6NkjDph6O7ubijwc889F+VXJPAODg7CEPoIP5JYPHc+\nnw/eDhQzGAz005/+NJSB/SWqYT39Ao0jL0QW29vbcSYZCu+oBWPEc41XZ6CkvKs0Qk6ga+9+gp9b\nW1uLEXnQDrS+0hxC5DQ5ORlGl8oLogAGlLdarYiGMDKTk5NRa8z+UZ9aLBajagGaA2fgMwkw0Owt\nRhvgwf6T5ccIw5+ix36QI+9B6I9xRLag7Xh3aDGvmGCd4Z0x+nwWPYHOYy8AYBjxJ7kubR7pH/3R\nHwV8Hm95lJTaGP+7hziOQFFckAb39HIGFEBKem2dT3TE0mg0gnBmM1BMPgNCoP0OwwVS9UylD67l\n950HwmgQUtFd46S3J8SYi1ksJselgAIg1OnmcZSEIZYUvCbOBfSIIPIzKSkSB3VAaXAvLvYPI0t7\nKd93cXGhdrsdCn1xMRrMW61Wg9eksgDjjpHCKLEOUnLSArNCQakTExM6PDyM6gUcNMjYxwQSQhJl\n8Dk3zN6Fg1Pz98docpFA4WeOkjHsFMUTPSDDyGu3243oBZTEs7G3RGoe9cDRYhScXmB/eV6MEI4q\nk8lEfabz7nw/iJj1BGw4KGBPoHdc3nkGale9VA0ky7pQYocdODk5CZDiR5I74GAPpSTBdXR0pI2N\njTjuxvXdK3+gwYiO/+zP/kyPax4vdWgJsJvFRShBZp6B9jZHNhgPwkaSxWMBXammp6djpBhHWXg3\nD7+DoeWgMg4rQ+g840cyiPBme3s7uF6MvqM1yoQ8e4mQ4fExzrQSci9PWhBeQoHwOYqi6ei4f/9+\nDK3ACTgXRjIOwSeZAXrBS3uIyNp4Nw9KgvHg+46Pj1Pcbjab1ezsrJaXl0MpOXJkenpaTz31VOy1\nNEIUnNVE2Mp/eQ8Gc7Dv9Xo9DOvS0lLMOQUBIWftdjuMqkcXW1tbqdpOz0YjGy4rGMr9/f2QS0do\nw+Ew1UGFswIhHR0dBbpkjSjPYTycl6BRV43hcMTOvrM3c3Nz8Q5QEOQjPKnp/D1ggwEnrBG6wPv2\ner3ImDt9Iinqqbe3t8OYYtxJGLIGGDSiBU/Eob/QAxhOjD/rD90E/+01tIPBQMvLy9Huvbq6Gjz0\nowAS9/8/E9qDvhBISqCkBLkQohJSAOsJ0yhR8UQUp19Ko81GiQgxhsOhVlZWVKvVogYTwcQwEK6S\nIVxaWgrubtzD9vv9KK25evVqeH+/vJCYZ3Bjz/uCNuFGMRadTieQHQLLe/vUJYaeSKNjsm/cuBHP\n4CgOhcTIcs3MzISxwBFcXFwEeQ+9QscX7+QcsZdhwSG6onF/hPX4+Dh42YODgxR3XiwW43wtFBza\nh+EbOzs7qYld5XI5ZAXkTluwlLTuUiIExQKi39jYCO4ZuUPpSYbihKXkTPWVlRVtbGykoh8MwcrK\nShhE34/z89ExIeVyOdXTDuIFFftEM6+y8EQTBoGopFAoxHvQPuzy5BUwfCf3gvpiT5FP7z5yXXA9\nZc0ajUbUE0tJgtFrpnle7zbyTjVkIJ8fzeWggcC5TaLP4+PjKG1z44wjIdo5OjrS5OSkVlZWAoix\nh1Rh8DxPZM+e6NP/ixfhCF4SWC+lJwMhLHzGjRgLjiAQCtFu2O/3UyPpMFQUY3vJD4vuLaoYBSek\nCc24lw+rADnwTggi6Pro6Cju4WUeCBjPxj08UQBJTwExF0IJXwwifeedd1Lv5SE4/BCGFaPTaDTC\nQMI5rayspNA168LxGTyXZz1Zs06nE+88GAxUr9ejDpIwjwqCTGZUY8oIORQUY4/RwwH7aQAYBhTu\n5OQkfk6ITEUEWWW4RkJsQtp+f1SMXqvVtL29nTq+YmpqKgwrYSB7TvcVxpN7QgkMBoOYAEZUg6PH\neCBHdLghWyBU+DwqUuDkuYfzgaA7nsXrRt3pYmCYXeBRYTY76nDzBA8NH9wbWfZI6+DgIEbp0UZa\nKpXiZzwrESc6wlxVkpCABafJWNNSqaR6vZ5KHGWzWR0fH0cjgaSQSQy48/SgeZw8a/OkffbSJYf2\noDGfgckLE+LB6WAM3LMTnkpJzSfK7sXCkMmSQnFYRGgEwkhQryeUPAtLyRTKwOaSgffElw9GgK8C\nRXvbJL9PeM+7s6n+PBwX64IFzcF54fV6XdeuXUvRG7y/l2WRKJAUoRGohTOZfO0xeO4MEEjCfxwK\n91ldXQ0DBCUAhwvfSjnSzMyMFhcXJSmGX7CH3gzB/FXoCNbHx7R5iI2skN2nXpd6yfn5+VDsbjfp\noKtUKnHsx3A4On/eD9SbnZ2N9WSNoI/4O/tHpIIsDP+nRAkH5JwiUcnZ2VnK0RDeU3+KoRpPosEP\nu3xAYTUajaBwQLjeUUUiK5fLhWNDp05OTuJ8LYAJ+umRSrlcjqQbk5pAn4ADDDIRCgYdGSRq6vV6\nUarYbDZDb8gLwKdCd0FVYOhByV5xUyqVYpLX8fFxIG3vrPr/JbQns/o7v/M7kkZn1ty+fVtPP/20\nPvvZz8aRzZL05S9/We9///v1zDPP6I033njvLzae0MNW2kEd/fEMZND5Gf/unhGFw4ByHxYVzzze\nqeSZP6/fA43hcT176BlPNyCTk5PxfnBKvCPCAxXhyRw3wh4Oe9KH5+EPf+ce1CX2+6OWS18rD708\nU889MH7UjWJ8+F3CKg+9cXYQ/KBcHMPJyUkIKIgRFO2fhbOEb/bsPMOv2T+UlhAWmSBcJ+uezWZT\nZ8gjL6yPVybgIMvlcig9zwk/SscTmWEOpfOohnXwyIma5PEIChnzemXkhfe4uLhIDWjme/h3svs4\nNGTCDQtOa3p6OigE7u0ldhg3wvb5+fnQLZ/JQHKK1t3xRhnWE2PIz0GKHsazhzhXnMb09HQkSVk3\nrxjwM+qxD1JyJhWGEwDgUS9rUigUokFkMEgfcz5e1fHLrscypF/96lf17LPPxpe88sorun37tn7y\nk5/o+eef1yuvvCJJeuutt/SNb3xDb731lr7zne/o5ZdfToWhfnlrGUrimVoIdA+1yRZLSvFB3IdF\ngitjk3yjEQK4HmY6LiwsBELAqPFfCHf4KtAVXssRIwJCFhiOCk/piku5BgkwjKzXnnLxLGSOSeLw\nX7L8t27dCsO0uLgYxhQE586ICIBCdugGUILPhEThpYRjZY08wYFAg7z5Tp6J0hKcDIYDxe12u7px\n40Z8XzY7Ok+JBBNzQJEfL1trtVpRj+oJB69U4JkwRn68BQhybW0tsve8Lwgsl8tF/Sanzjp/yjHT\nDLPxDDnGiu92I8AzYnR9/2mNxBB4Z082O6oIabVaKc796OgojJyH60RhoFLaKhl44vkG9hQjRkYd\n5AsFtby8HM9HdIExk5LDFqGvxkudmCYGiMJhYgBxZHD++XxeCwsL0R6N0/XmD5Ja6DoGGd1Dd4iW\nADfI73vZrfe6fqkh3dra0t/+7d/q937v90JgX3/9dd25c0eSdOfOHX3rW9+SJH3729/WSy+9pEKh\noOvXr+vWrVu6e/fuI++LN5QUm+1CzeIA5Wldu7i4SBXjYsD4A3rEk2Lk2Eg2z/u94aek5AA75xZR\nfu/QQJgIJ1FOSfFvCDnHPuCdQeJk2wuFgiqVSqrMxAWNkNQzoxg+TxJ0OqPDz/iZJK2ursZRHSB0\nHBYGIJfL6f79+5KSsiWcEWEYCQdXcEfMGD0cBoaCfno+Jykl1IS0oIFSqRQT58/Pz2OcX6VSSSFm\nEC/fAYqi4oEuHORlvJCcyINKBzd67XZb1Wo1VSbjWWqcPRQA8gYH22q1gnPEcXlZjX9+nKZBfvnd\nTCYTTou9457ukOBNQdKLi4uRwYYyIDzGIc3MzGh5eTk6pQAv2WzSPOCVDURVGCnep9FoxL47dcN3\nw8lD43kEiswwd4K1lRJ0iSOm3dePYkFmidBYg4WFhdRsVkCYR5W9XjInGG6dZ/ZStse5fqkh/eM/\n/mP9+Z//eSiBNCr1YMRWpVKJguWdnR1duXIlPnflyhVtb28/8r7OtZF19Y0DAcCr4K3YEC9/QAhB\nbZ5NdK4TQwaF4PWAGE88m3dU4Um9fAVUR4cQyBCDxnNz/AVeEMWAVxwMBpHk4D0wepDiJAMIF/lD\nkkIaGVaOd3bknc2OCrN9tuOj0PHq6moo7rj3Bl1hSKQkyeUjzIbDYZTsILCuVKDt4XAYgzZQGNA2\nBhDHCTKk8YLnI1FByVWnkww6BmEgT6wN68/6eF85e+rOEcoHI8h+cWRHp9MJjtY5c0J11gq+np53\nZKrVagVYwFiPI3scm2fFKaRHH/h3KSmq53c9uYPe4JzJaPPMnBQ6GAwirOc9ieAkpYYHSQn4IER2\nhI8+sUY4Aqc+QJ0MgPGIB+eUyWQiGeoNCHwnEYojdhzowsJCDCSR0pUl8NnIRLfbDTT/JNcvNKR/\n8zd/o9XVVT333HMpNOQXC/Je13v925tvvql//dd/1Q9+8APdu3cvPIHPLBznBkGUCBolFbSOgTAR\nBhYVj8ricOwxaIMNdyRFHSlCwLNgiJgF0O/3tbCwEAaC8J+wjU1zHgtjCwLAkcBxEn55twv39YSc\n84M+0ML5XdZleXk5yowkhQenygF+zcMvnMv29nYgMOdT+V3KjPjOTCZpJUT5MBDs58HBQVAxfM4z\n8qAI+E6UhiM5JIWBx7iyb8w8RTn5OygUzhHD7O2YKDrVEoTwGB6Mv5cCuUOQkkHIHt10Op0UN024\nCjqkmgDAgjNCljFcvKMnAdvtdvy7UzCuB8iQG3opOfJHUhxAyGhDKZlFiszinED96CYG0cNmT+Lg\n8Jx7BPAwvxadARDxPsg47yAl9BMy4mVj/B2wNTU1FZGH022ejJWkn/3sZ3rzzTf1zjvv6J//+Z/f\n06Y96vqFhvSHP/yhXn/9dd24cUMvvfSS/v7v/15f+MIXVKlUtLe3J0na3d2NIyQ2Nzf18OHD+P2t\nra2Yoj5+feITn9AnP/lJ/fqv/7o2NjZi8YDxXk8ICvQTP1lMSiUoi/AwH+TiyahMZjToAC/LHwwu\ngogH9EJlnzLlCQwQGdUHnvxBSEGloIiZmZngorztEG/tiQOQJH+4JwJKKYijRdYNdMfzYThAN9Vq\nNZA0VIg/Uzab1ZUrV2KdeH4MF6gbw9BqtVQul6PzCqXGoaJwxWIxwufhcBizSLvdbpz9zrNQctTt\ndqOgmndFkdgXSSmZIfJxTr7dbkdNIrQLCKVYLEb1A0Nqjo+Pg9Zw+oRyPQyEH6eBEaYKgX3CqPt4\nPinp7mMNoLEwRtT44qx4nkxmVEJGwT2OUFJqgA3rzhqBat15DQajOa8zMzNaWlqSlLQ20zzBs/Iz\n5ILkJAjPOXh36Mg2RpG98jrTfr8fFTT9fj9Cbt9T3mlzczPui8yQW6ED7N1339Xi4mIYfkJ7dAMj\nvLq6qt/4jd/Qpz71KX3qU5/6Rabx565faEi/9KUv6eHDh7p3755ee+01/eZv/qb++q//Wi+88IJe\nffVVSdKrr76qz33uc5KkF154Qa+99po6nY7u3bunt99+Wx/72MceeW8MG8JDPzZIz6cw8Xl4NOcw\n8HDuoRBCBByk5QKEQKLIIGI3hEtLSxESITwYTpAzxo1nhqznuTA4JCh4fv6OwceJYPQIvwhXvUCa\n9UAgz87OtLy8nMrqOpKRkpM/SYRhFEmYIOwYHRAia0KNIj/zcjOfswkNARfHHnh2G7Tg/Onc3Fx8\nz82bN5XNZuP8K+a/ElKjCLVaLVV+Az0wOzsb64RhoRUVJwm6I+RGRohcKN/h6Bk3FHQ9+fwHKBqP\nmkqlUhhkjPCj0LOjLo94QFPOPTrgwACxl+N0GMibBJfXv+IEdnZ2VKvVgk/1apKpqanQQ3hfKZmj\nyvNKSZ4D+aZEDdoLfhSD7vSC0wDQcRTRe6MNEYzTI9vb27G2zsET0dHVRSkZNA467gdOEl1BRzzJ\n9UQ5fjzBn/7pn+rFF1/U17/+dV2/fl3f/OY3JUnPPvusXnzxRT377LPK5/P62te+9p6hPV7QOzGc\nM8F7omigH8+64k3491wuF3MQvf3Lyf6lpaXw/iQL2EhJwcu0Wi1JyXHQ9GZ76EU4z3d4SYtzRnw/\n3wXK5DvZSMIzR1i8C6GRpKAbnNuRlEqygYhQLlAzSFNSGGl3LLyPh+FeQsKaE+6xPq4Unc7o9ADK\n4lgrQlGqEzBonhUfDofa29uLBCCKQ3UBCNK5V6/gIDPOcRVws9AfniBDdkj4wTcTwrNePKukFMJC\nBtlzp78AB/1+P44j8dAVmQel0qHjyQ74Ys+iu/y5/OMMCbkxYm4QeAf45YuLC62urmp2djYSRp7E\n49nhGdFF5I91gTIhUkBueDeSfi6P8O0U+aP/6BE0iEeE1Wo1nCRyg3ElgcV++DQp5LTb7WphYUGN\nRkOTk5Ph+FyvvbLiSa5LHVpycXGhubm5lBfCc+I58PZwTF5gzcZ7RwcLORiMjgOen58PIwKUx2Cw\nQSwkiQkGYLTb7RSxnclkYvF9SCzCDEcjJdOtMMwIEKgOpXFUWywWYzAD3+ce3jlUvtcnkXtybHZ2\nNp7VDcvs7Gx4YYZmg64ROilBHayVpCi6B0mhkP79GCBCMIwXYwhZU/YSo+3G/N///d/1a7/2a6nO\nI7K6GHQ/LwtOmc+iHKBUlJKqC5I8lOs4hzo9PZ3qqfe2Rk/84HAkpeZjOo3kCU0cAesNIiYaomV5\nOBym6lwdrSHzrVYrJYPkCZrNZpQWEdYOBgPt7+9rY2MjHBPHJpOc3N7eVqVSib0bj9ZINDJ3QRoZ\nHLhjZByH6ZQUcn9+PjryBtmgVdedB/QKff7UeZLkw7BmMpk4sscR62AwiJMT0C2Mvx8pTdTB0eDk\nVhiEwjr/xV/8xXvmhsavSz1qBC/tRcyPIpNBfE5eS8lwE0qpPGQbDtPHEPgsRAywlMy1dMQ7MzMj\nKZksJCV1ZUwT4jsc5fhGecgCz+NZQkcKnoDAYEpJG+35+Xn8ATn1+/0YVEyIjIfH4OAsQMbM+2SN\np6enY7DF+HN7twsItlwuR9829+D7WEevSoCSoHCcfWy1WqmBMx6JFAoFffrTn/tSgVEAACAASURB\nVFan04n3433a7XagaKoaQBZUO8CNYcw8rEYO5ufnY4g0+87wF5/VQNjOHtXr9UBqrBX7wzN6bShG\nBEfKWhLu8ozdbleVSiVK2rypBFrDK0Ood/SkFhlpajIBDJTWEQFhfKempsJglctlZbPZGArjkQgI\nkbB+YWEhNWAHqsIjKGQIcJTJjDqw0NtcLhc0C1EaFSJ+2itriDzxM+SdvcCZ+rxSwBeOj+dA11ZW\nVlSv1+MznU4nKk6csnjc61INKQrKQAkOo/NkDZvhSJSwGS6Jhet2uxFOwQG58LKYhAv0p+PtNjc3\ndXJyksqASkp1HFFewuayMXhUhBfhAFX6JB6MMCgZI+BombIPnAcoVEqOP0ZxMUhwfRgOkP3ExISq\n1WpqLilcXq/XU7lcjvclvOWdfYwfSsugCSa993q9mNDDnkLmS4mjkBST26V0uyoohoHV2eyoS2h8\n8hTtmSg2DtOVn3VCQVwxcGaM2PNaVGp9cVagSQwM06Qo0aMyADkh7GTfcCbIKdUZhNEcB4K8YwQd\ntfKMmUwmHBjvQHkYYALkDYcLncEzIKc4XSoziERIKMER8h5eY312dqaNjY2QLWTT5SmbzUaWnCSw\n6xPrxh4SjUmKsiu62bwUEIeHHuH8cMIXFxeq1Wo/FyFcvXo1wAdUWbVajejX8xL8wZk+7nVphhTP\nmslkorWPThqMkocznujxkJnMPcLoGXc8J5uBwgL73YuXSiXt7++HV+IZpYSPGR8Uy9xRPgfqkUYC\nReGwD5+mdU9SFG+zFp6pd4QIauHn3sHlXPP09HQM8XVCvtPpRBYWRwLHlcuNziRfWVlJccus3enp\nqc7OzuLIZzeWZIqldAThCQFHLc49UzuJcR4Pgd1QMpAbBe92R0NIyMSOtza6w0HBeS/aOr0zjL1t\nNpsR9no4jfEaDodxOgBO3/v+MWbUO2KcWHeM+sLCQoStJFBR/Lm5ueAgMX48H8emMFez0WiEzvjw\nDfYE9OZ5ChAyMpzNZlOHMHKcB11P8M/I9/n5eQzEwfGTiAWFdjqdoOzYP0J1ngt5wOHwzHSlOT1E\nZILTBDhQVcH6l0qlSBghC6BpjwDhSqE20EvW1Uu0Hve61MPvUM5WqxWTcQgLvNwhm82mvKcbEQQN\nzoifEW5gnEAQhO/waOvr64FoMBRzc3PBzYIKQEgYNjwyxd/jiaZsNqvr169reno6Nh9UISXlKAgN\nRhIB99AGfhMnA7qi2J/2Pjo6ut1uKAycIJfXH/KZTqcTmVtv3+N7+v2+1tbWghP1pFi9Xk+FQc7t\ngmT29vZifRBYDJEn7FC0drutcrkc9/OhMxwlQvLCZwI4BcRncNCsayaTifZZZIxnQd64UEDoAt5h\ndXU1laGXRsarXq/HfjFzled21ASvJyXDtTHKrBk/h3NE3kBi8JPoBvuVz+fjeGu+03UAAywlySsm\nckFbEQmQp/D6TxyY0xoktxYXF1NHfA+HSYMGn2WveD4fbO0RKGg6kxmVd3HeFTp5enqaKocE5YJa\nC4VRq+3R0VFQPzhY9gQghcxS5VAoFLS2tvYE1uwSDens7GxwkcP/ybp5GEFSwWvtCHHm5+dTCQGQ\nBZtB5piFh2cBrSJcp6enOjg4SCVI8MaDwUBPPfVUfAcojiNKEAaQFMXMtFOenZ3p/Pw8yHkpQWEI\nkPPBhDcYZs98gjAJkTHAfsgXXpl/Q5ARZik5DI8/kgLNQHPs7OwEUpRGofj6+noYct7ZS4oIfSVF\nTaZzszdu3Ij3vrhIzkP3zC8OLpvNamVlJYb0Ot/qzRYYdJwo9/bEAbykn9EFEsKpeTkWyJ9D93Cu\n/LvPOfAKieFwdFbTysqKisWi9vb2YioRTh30xjr77zpnz74dHh5GtAG3i5EZDofa3t6OffW5ssgW\nDgp5kBT7Sg0zXVnT09NxcBzGCx3j9zn0j0E0UAgguJOTE1WrVR0eHgaSRU48eeqRpjfBkAhGTuCP\nyXmw10QKgAiiESgnnBXyyxAa1o1987UhEsvn84H636sj872uSzOk3mvtg0kIIzB+rmR4qePj41hA\n2sqkZCI2hpAN81pRUC3Zb5JBXuyMkj948CDaN1HW09PTCK29RIJ7gSKYOkSrJuEThp0CaIQbocW7\n0xk03r5IaM6ADoy6l29ATYCofFKOC7R3oVA+srCwEEJNlYLX3qLMrGmxWNTq6mo8h3OGZK6Z4uPK\nguEa56IvLi60tbWldrsdh/aRSDg5OYn5pjMzMxGGOjXCGsCfdbujmZ2VSiX4ZFdYHLdTQ71eL6II\nMuLO0VOfiFJLyZCWer2upaWlVCjLhZHzEjLCWuQY48nhgLOzs8HL05LqoaoX/2P8ce4gabhVIg7C\n8bm5uXDUc3NzQefgnAeDQfCb8/PzOj8/1/b2dqqCBPRJlDYcDjU/P69WqxVDaHA8hO/+jJ7olJTa\nR9dhjKF3Ny0uLgYfiix7+ZZHHDjC8YgWSgUn2e/3dXx8HDz+416XZkhRLA89i8VikOyUKKB0oEa4\nJRSd8A00h8EhPKFAHOPqIS33kJJpSh5K0Z8tKRIEklIUAxuGATs/P9f+/n54R76TEhsMK4IE5weK\nhljnMLhMJhNcq3trKTHe8JE4BS7WkuM8UFqE1rOrrAVeHgHGGDjKd0oFFLi2thaIgTU+OjqKvaMl\n08MrLxUjJJ6bm9OtW7ckjQaueA1lPp/XjRs3UlUErjigJqiSs7Oz6AiiUwa0irGkhOro6ChFpwwG\nAy0sLCiTyajZbMbegM4kxYFuGAmPjpgJiszwziSreE7Wxh2i01UU9DPpv1wua2lpKeSHdlGaKPjj\nmXYf6oLhp3nBq1Z4NknR/UU5HkaIvICXGYJUnQJg5ifPgvwDdAAuOEIoItAr94K7hvo4OzuLJGez\n2QwkinMj8eaXV+hQ4ugJY/SA5+Pk4Se5Lq2O9A//8A8DiboCEpo5t4RxAKWyGYQOFCG7RwU9ec8z\nIR0K7PNHx+v2qCEcDodRKtPpdAJdORoh/ID/W1pa0tHRUZR8EEp7Lax3gqCICCZGxsPyg4ODVGsn\nnlxKUKgjTZ4DJ4NSsT4eMksJvcDaY2yZuEMpj5T08rMOGHcMJY4I5fKidj7P3jnqwHHwftAHuVwu\nNVyEZyMJBHIEAVNXyrNieFF+fl4oFLSzsxOOBgTH+mMEvJAemXGlpboBPpF1IOvNXrMGGEHemWn+\nfBelbXD3vt/O9WJcOFIEowpSZOCLn0ePI2StfN4B0RF7B32CIwItOp87OTmpw8PDkDXeDVnCEUgK\nUIPhd4Pc6SRHOruuQ5s4jXV6ehp5ARwt/wb6ddoM3fLkq7+DyzNOpVQq6Stf+Yoe1zxeGiJ1HpCX\ndi8Enygl6BW0iMFDoEi0oACODry3GVQBEqamkcJpfoah7vV6WlxcjPrLbrcbaMDLqFC0brcbiSE2\nxREAm4mBRMGhKbwAHuMOZ0ufM0beM948A0Kcz+djfoEnzDgADB4T54LCd7tdvf3222HAaCHFw7PG\nTp8gaBhPkiT0TpOkc8foiSUMDsaG4m2fI1CtVuP7qLigH59wnd75hYWF+Hkmk4mpVhhmz972ej0t\nLy9rc3MzaB+MQalU0sLCQijaeEnS7u5u7L8nCDG0GCcQkLfYSoohL6x7r9eLIdi9Xk9LS0vxDm50\naRnlJISzs7NUQrNQKETITlhNfSxrW6/X430AIqDoUml0nj2oGxm8detWylmynw8ePIj2ZCgrR7ms\nqzdLIHtuxJER7ALfm8slhwCSMyHxRESEXBHRols4IEr0oAZLpVKU2RF9uS3yqO9xr0stfwKWu5dD\n6BAEeEPqwJyr4uW9792NKJ4MZIrgoCjn5+dRrEzoieGanJzUlStXUuE3vBBhiYfuoA2EAI+JghGq\n1Wq1ONmS98CrMu1JSlAfG5rP58MAupFGEEG8g8EgVYvpQukZfYwo64RxXF1dTXFktBF2OsmRFGR4\ncSg8Y71ej7XzulmiDhIAHkl0u93I6heLxZjiXiwWQ/HW19fjED32f2NjI5wozhfDBLosFot65513\nIlTEwDr9kc1mY/g192Zffc6mlNA/7XZbV69ejUoTWkBZZzLRKDP75G21dJctLy+HE8Jx4Bw9SoBr\nZo2npqaC15RGZUOMt3PDLiWnFRSLRS0vL2tpaSmcsDtkjj72EB99+tnPfhZgBsM7GAx0/fr1WBtK\npfhuHGs2O6orZW1Zd2SBiJLIieeamJiI2bNw3TgjdJzyQZA3UQHfAWrN55O2005nNEym1xt1suGA\nkEcc5pNcl8qRgqCkZMoMSBCeU1IMLwD5SQrDKynFv/lpiXB/GCSElXATD+tTm/DWnEpIJtPLPVwp\nMfJS0svs6NJLsUhC8TOeiaQI6EVKhoyAsJmPSmKCAQyER6BszjoiRPeEBp9jrXh/Qj2MI+uPgUbp\ncXj8F+WRRghrfX09hLHf70d5CkLJM7HPGIunnnoq6v88W86zZLNZbW1txQxTlH84TOo6yZBLCqNG\n/Szvx/d6qO60BApHFIGR9rpM7o9c4IRJSjptBNqhLIeMuMsJ0ct///d/pxKPnjClJZRhxQANUB4I\nltZXiteJamjx9KoRLqenFhcXtb+/HwlVDFi5XE5l3p966qlYe/hmDCjoGYMFbQW/6cnOTCaj/f39\nCOt7vV4qL0I1CIaS0ZbINdUCyDChOnLF+56dnWl/fz+ADeifI9p5dhw3AOBJrkvtbDo9PY3MK/Ca\n0AlhwwjAb9CznclkYigGZTKSAhliQPgsiksygY3ASJTLZZ2enurDH/7wz2XZMW4kizwEGQ5Ho7yG\nw2EoCsab5yahICkMFkiH8M9LYbwgWVJqFgHK3G63tby8HOHaOPpAIBzZ8t9arZbiSScmJrS3txf3\nR4GlZPYrPdBwkcViMbpq+F6qAyRF8ojp/Lwb74eSHh4e6vDwMBAqTsl5QPbj/v37YSwI00ETvV4v\nkgTjSRX2AAWXkhIm9p97gYI8GsD4we9JCsPPfkNVUJOKIeT3oRZA1W4oJemZZ54JQ+Mym8lkIul5\ncnISPCiRi9NMpVIpDnPj+zxTncvlYkAI30ONJUk0JutjlC4uLrS9vR1hdT6f187OTlAaLu+sryc+\noc0wvESD6DuOjvWmDtWpHNbcKQFQ961bt+I7QfbsL0YXzpdIhQqDfD45kgWw5lHJk1yXeoooY9EQ\nYuA6pUEkZSj6lZKauUwmmazvySgWg7NoUAYMBJvGvT3EX11d1d7enmZmZnR8fJziPllglIfRauVy\nOYQR4SBsRlGY2UqZEVxwoTAaLoJ35l1AJXwGXldKJvcM/yfTyQxMEAjCD7LzhBBCeeXKlVSCrV6v\nx7G7GGH+zasaQM5QIKenpxFyghzJRmMsaPkDxeOAKMe5fv16JBygUaSEuvG6v0qlomq1GgiN76AU\nCoMMEiHRQEnR3NxcoC2MAE6N32u32xGaw/dxH5I31Dt7tp7/Z/1RaL7f0THUCWvMu+MYoaO8Mw/6\nh3uTqGUPuN/8/LwWFhbifKzJycngSD05SQKQ0i72Ghnl39/3vvdpbm4uvpOqE+phkQlJ8UzOl6N7\nzkEfHR1FnzsDrT0KQE/ZYyIU1tdRO4NH0DloPk824WSgmDzB6Yk1SUEXjZeu/bLr0gwpXh++iKTH\nOO8G8nGhnZycjCw9BovkCGEDAuJcj9di+mLn83n953/+Zyg4XCFezb0v3r9cLodBRkDoMPFynn5/\ndPYRyo2XRGBpiSPh5IbYOVaSFX5m99zcXIR8w+EwsrcovzsZKUn0+CzUjY2NQN/eWcO7glolxSRz\n7lEqjc4qBwmCQlhjDCfKQugOAqzVajo6OooQmX/zEiv2iXV+5plnYo2lJBNPvSMNDcgIThU5YSpW\nJpMJSgBF5Qx2KARmEIB4JicnU0YS4wC9AtdHmydOjkHifpww9aTQJNVqNZy3G0aPSpCnnZ2dkCfu\nISVGttvtRs+8UzQ+10FKDxT3fvr5+fk4RHB/fz8cGYgWQ0ZLMjIGtUHSEGOI8YPTXV9fj1bparUa\nzygpFQnyXjdv3kzlOVqtllqtlkqlUrTzeo04NbXIEMN2MMokOKm/RZ8AA7Ozs6mZto9zXVr505/8\nyZ9EjZqjMchfhNeJYxQbBZUSo4WXxXO7oOFtCRnwRAgVm4xAsAEYNW8YkJKSGuebEGBHgHhi5+Hg\nblAgsorT09ORURy/Nw6nWCymJvt4UgE0Apr36gOez1EjBgKE5gS+UylOjzhKJMyCf/OsO/waCupJ\nFOgPnpE1511ZLx+mTATg1QZ7e3u6du1a/C7PBq3DFCEMLUag3x8dx1ytVrW4uBjIi7CVe0lpJMXe\nU8IDb8q7ZLOjJoB6vZ6qU3S+rVQq6eHDh5HERJYwBNwHuXV6APnj54xGbDab4QC8MuHk5ERzc3Ph\njIkyWAvCWjr1AB7kDHAAVC64npLQ4f9PTk4igeXr6DI6MTGher0e1AQyyxqh/zyryz+gYLz/nlMY\nPCfiuuMVPHRwMXcDI+1IlOeBSvvLv/xLPa55vDREikGAtxgOh0GmYxRBSQ69MbYoqKTYQBJAlCGR\noEI5SKIgAN3u6Gwi74tmg0G2FPZjSBAODA5GFy6X1lB41HEOlOEblHXkcrlQBDYYQfOwBgPsHTBO\nxA8GgzgREVQBfwjq4vfq9XqsDVPQQZ7U90mKxJrTDDi6ZrMZfck4homJiTB+/BzkxWQj9of1lpRa\nS95lYWEh+CxJKQqkWCyqUqnE0ccgIULs09NTLS8vRxndcJjMcaXUil57KAqG1nA/OEhaf3l/Mr0o\nnaMxbwuVklMwkZtut6urV69G9pksMr+LUR0MRiMPvaAfhOt0FHNzkS9pZJgZ8kGikIYCKeFrJyYm\ntLa2FrLNhWFsNBpRG+rUCygOJDscjuqsOa0A/tF1CCMIQuc9iTSGw2G0m2I0PRlNUnV5eTme5amn\nngrjBzig64tSL0AJz4Q+gKq9LAqqD53yqWePc12aIYWLkxTKT1jqWWMf8IwieYEuXhy47plZhN45\nTL6LDaXlECODgZWSSgKfSwrvJClloPk7xhcjiOLjHTH+jrhBDWQOES4QJF7Wi9oRVPfmcF6ETN6i\nh9A3Gg3Nz8+HMlDIfXBwEC2IfBbFQLgYFFEqlVQulzU/Px+JA+dBpWRiFqEq3KI/q5/UCPIElZ+d\nnalWq4UC8x4khwqF0amXe3t7IQtkX4vFYmrIMSiLHnApKdfxQwkrlUq8AzyqH5RH0oxhNfSnOx/K\ne4NO4XjZcyip4XCoWq2WquwgasEJF4tFHR0daWpqKlUnOhgMVKlUwjmOH17oLaPZ7Gg8pFc7XFxc\nRKs1n+e5BoPRbIS1tbVIjhGBeBce+4VhovKFyIp3hmt2yg4dkBQAA6fBehAFAIQ4xploAOOMTnAv\nN/w4bS8ZzGTS9bNeBcCeO+/7uNelGdJxbsnDe0eaoDzCDsI1PNU4KvDuC3hG6v1c2UA73qniwsii\nZ7OjFlbCU54LDw9/SVgPQvBaO4SIMBnCnufgHfg7BoCkgTsML1Z3ntfLNjAQdJXAt7VaLS0uLqbe\nDfS4vLwc6Jg9gBN0AUUJEVJPbnE5wnQntba2FqgVwXWKxSsWPPSbnp4Onpxun0wmmfF6enqqpaWl\nMELIj68R+0/WWkqGeFBVgXLitEFTcLxeOjUYjI4sJmRF/jxSIHmEgSAxhrFYWVkJ2aJkDSTWbrcD\nNYIy3ekSmeXzyRxakCzOn5I0Kanb9AQuZU0e0g4GA7377rvRKIDhczqHiModXy6XU6PRCDSMUdra\n2oqSP5clj0b4Xvh/z/7zjv4s/X4/HD4oVlIk1nCWc3NzEQXyPCQO+RwJRk/Ooi9Pcl2aIWWxvTYP\nxEI9F7WUGAk8yXA4jNCf7C+LTB2pJwSc54OvgbznBExXQi/n6Pf74f1BRM7xICAonZdIgWTZWIjs\nTCaZeo/H5vkxmggPhplndMMLeiKjTJIC4URpstmsGo1GJIpYQ+eWQXI4Bo5y8FrFTqcTxece3rGe\nJBq4MEbSqMXV6wLhu5if6iE1ikGzBEiY9fE6VX7v7bffTnWLSckJCsPhUM1mU/Pz86kZsjhHLwLn\nOwjtpWRYNE4A586BcMx+oJ337Owsdegcjh9jzFCPer0ev8P7IIfQB4eHh5GIJHNOmRYdZHSfFYvF\nyNoPh6Ni9VqtlmpT9WPGScLNzc3p6OgoEnAgvomJiRjXR/vseK6B9YROGgwGqlarMSt1Y2Mj5ALk\n52gPBCgpplKNJ4aldFuyJy6hCL2KAx0+PDxM3QduGR0AtDz99NOp8B6k/STXpRlSN3gYE0lRjoCB\ndcIdg+WC1m63U1lfzph3UtxrCjF8FBPjUb22kY0mtGBoAp4SY4N3KxaLYcA8Cwiywsvx/RghKWmt\nhN/lZ1487PMhUXAcCwYdBUVxERTu54iENkNQLsLqWV/KZcZLtdgXHBTGhw4dkBfrz98JLzFU0AGE\npTyXJw25D7wZPDFrh5MqFApaWVlRrVbTzZs3w4lxXxyeH93MuhHSsgc4QZwp/DKyR7RRKpXCsMLh\nO+1AqZCXYIHQ/ThoTwzyd5BnPp/X8vJyJLj40+sl7aSnp6eRwQblkTMgfMXgYFQ8n8AzXbt2LY4t\n5t8AIKBVjwYxttyDKAhjvbCwEAYSQ080OB7mUzFBiE+TC885GAzivdkDIkAOs6vVaiFfjNjjud2e\nsB/I1vn5ufb29qI4//81936pvfZnZ2fBM4G+UH7PdiLYnhmXFOUsg8EgkCLZRS+exlCAwBA0vKSk\nUBB+RqIAgccAuEHyASZwhxhavo8ODYwyf5zzxChRzI3xpfwKXmh2djY4KVAnyAekhlBDa9B3zb15\nLwwWDojfof4Ww8HaQNCTlSfMwyizNigMKE4aTWnCURKWk3giTESBMe4kSFAOhpa4UQWNY0SGw6F+\n/OMfB3ry5gDWEgWTpMPDw1StMXspKcI9UDLomooAb22mIw0jDw+HUeS9OSYc+cGhYCglRcThRz4P\nh6PRdMgBiJ2+csoG2Vs4StaSEsJ+v6/9/f042pt9arfbOjw81JUrV0L+GaQCKPBIhn0FgOBw9/f3\ng7Mlsuj1ejo4OIh7ekWM6xSfByThaIlIeWcqbHzATLFYjGjLK31A3oeHh7EuyAR5DCaoeaIPlP0k\n12MZ0uvXr+vDH/6wnnvuuTinvlar6fbt23r66af12c9+NngiSfryl7+s97///XrmmWf0xhtvPPKe\nkPQogpP6ICxIYjw2L9vtduMgNMo9vIQHpWEALffi9/kDR4tBRiAQAJSR+/JfPCoemLF33gLIH+80\nQlCcb5WUQiV4aAwIRpje4FwuF7M1UTgn1z05hWFEEVkHR4z8F2OO8jpicN4RAUZB3TCA0lhnjCfj\nztgrsvKODOgVx9GBrnEQGAXuSYkKCUNoFUpjDg8P4ygJKUkEQUHkcrlQPiKXbDYbSBzqSUpoqHa7\nrZOTk+CEiYRYG6Z/YSTdsU1NTcXkep4dx1Ov16OaAEWHboGuarVacdIna49TAE0Trfi0JRwMhoiW\nWq/3JGGHTqCXOG32G1oDNE/VBvRTNpvVxz72seAlWXvn6T0BhNPydULH+EPVBbJJfTB6ivyxJ6wv\nUYSkKAObm5tL5QWkZEiJI/Tj4+PoZnzc67EMaSaT0T/8wz/o3/7t33T37l1J0iuvvKLbt2/rJz/5\niZ5//nm98sorkqS33npL3/jGN/TWW2/pO9/5jl5++eVYUL/cuJBhxxuzWXBVp6enkYVlofF6bAre\n9d133w2j6soOSiQji0H0LiQUG2RLWRLcC+GXpGipA4nCW3nI6+gSwfXEDZwSPOF4N5KUlETxs8Fg\nEDWCKBGGlDBNStBwoVBQo9HQ8fGxpATds8YoGKU7XsqEsIM6paR1jhDPWxBJADJoYmpqKg4Z8wQD\nRhUniYxxVPODBw9S7wMCJXwk+SQlw1zgR9lvpl3913/9V4oyYF24p/e1Y6Sc00PeoAXm5+ejW4vj\nUKTRvFr6t5ERopbj42PVarVQ1KmpqZhADyLFCLNGhKAkU5BZ7/oDxWE07t+/HzygF8Kzv3CJRA0k\nUq9evRpAAePMvna73RjqTARGgmpzc1PValV7e3sqFova2trSd7/7Xe3v7+vmzZvB46ITcNqsjcsq\nxqxcLocjB0VTguYDXYbDYTQdSIp5rdBJ1Kh7NQ4RAtRBuVyOYSo4RC91e5LrsUP7ce7g9ddf1507\ndyRJd+7c0be+9S1J0re//W299NJLKhQKun79um7duhXG1y9eCB4Hg+MlMnjFycnJ6KLAmLDReC1Q\nC6U9IMFOp5MKKUBZbAhIBT6G2ZSePCEZ4skOQhhQJsaW5yExgGdGMTGQ1GLyOYw6xhTDA3pysp7M\nuaTob+ddEc5xPtV5QT7H33m+4+PjVHJGSoZaEP6DKjG47AFOA6GlnhLuFKTKH4wXlAyhdC6X06/8\nyq/Ec0k/3zHT6/XioDIvlYMT88EwH/rQh3T//v1IkkmKZ3fniJOm1pHhFzy7Z/15B47Yhs/EcPCu\nTGhaWlqKkjEK35vNZlAyw+GoLM0Ng6QoY/IEG+F7v98PxI1zvHr1alBgRFvIejabDZ6RvXF98QQp\nZYKDwWjGQb1e1/ve974AOVBSb775Zhi49fV1ffSjH9XFxYVWVlZ0cHCgTCYTRfMOIvh+9AVHy+Vc\nLoCjUBidGnF0dBSfRw5A7ayVpNT7+vAZb2ZhrCQnQFC5wRo/yfVYx+VlMhl95jOfUS6X0x/8wR/o\n93//97W/v69KpSJplF3d39+XJO3s7OgTn/hE/O6VK1ceef7Jm2++KWmEcK5du6bNzc2A9iyUo0Wy\ne47OUBYWx89cd34U/pPEBD3qfD/fwfd7aQYlGNVqNc7ykUbUBtlRN8gYEkc2jnJ5bkJYlMBRHUXx\nHmoTPvIuoPXp6WlJSqE7KRnegiHA6ICOMRp8H4YIJ0I2nAwppVicHe7vn00TKgAAIABJREFUzZo5\n+gYZ0ZPNXtEl4xyh1+6CGsc5NNZ1b28vznTiBAQyt9QaOt9K8f7JyYlu3rypd999N7XWyBrOG4RL\nMo8mBx+0QfjL372uGDTlzpSecn7Ge/J70Dggy6mpqciiu+H0qAoqiRDXZYUoDSeD46dmeHFxUbu7\nu5E09ZpTnpEoBEP38OHDWL/Dw0NNTU3pxo0bkel/5513tLq6Gtwq/f1LS0sBkDDwyInz3I1GI04e\ndaqFz+JwQanck0qe09PTSDRLCkfCZ1lbWkN5LyqDcrmcDg8Pw1aRn3nc67EM6Q9+8AOtr6/r8PBQ\nt2/fjn5nLozDe12P+rdPfvKTYUTcgKGIQGtHkCjryspKFBOjECiBlD4KxFvjJEVrGQuMQhOCeKad\nDUBwpaSk54Mf/KDu378vKTFiGH6ek2yk86wYbA/hCOsvLi7i/CMIfQwIygAqIwGDB6U7h+wsfckg\nLE8SEA56ucjx8XGcJc7zuaHt9Xox9ENS0DLOqzG+jbAblDM7OxsUCPvD/YkuyNp6fXGj0Yi6S9YL\nxWRvScq02+3Yn+3tbWWz2Sg0x+BRIsWzupN2RI2jJhvuw3+hlLj3eLKOsiKMZD6fj9NynQdn9m67\n3Q4jAUVzeHgYtAzyC1hAbofDUUH//Px8cNAkapw7BYHCMd+7dy+qC+AyncqhYoQ18LXf29tTu93W\n3NxcqkxtcXEx+HGM6P7+vtbW1rS7uxuygvEb12dACjLphs5Llfg3wIzzxZlMJk6luLi40PLyso6P\njwPUIC/IKugYQyxJ6+vrqlQqwVc/KpJ+r+uxQnuKeldWVvT5z39ed+/eVaVS0d7eniTFdCNJ2tzc\n1MOHD+N3t7a2tLm5+XP3xFhiXJzAd+HxsiLCOgYVEApibAjHWGCQE/8lCZXJZMIbIqyMBpOUQoie\nfcagtFot/fjHPw6kBOp0DpNQDKPAv4NsEARI+mw2G4MS8IZOyHvTAuEbnyXJ1ekk59eTleSQPNYP\n1ME9h8NhzGClTIayHtYDQ4xAsyZk8710iPXluzBUdJBQJyglfCt8H5QDaw8v6FUEjp5zuZx2dnZS\n2VsQutM1GIhCoRCzKev1uubn51OUD5GATxujcoMEJ8k/6g1xwD7JjOYB1mVxcTEaHTCWyB37zJpT\n24iTAC0T8UiKaAQDSfE9BmC8dpr2VwyMR2zFYjG+h3XEmQAiSCjRKw+So1mEiVwHBweBDtfW1oJX\n9pIjUDp11ITdGE0iIOf7WW9oFaJMHAZAjAi5VCpFIgxQ4DkLbApcMDQYMvWonM4vu36pISVjKI1K\nQt544w396q/+ql544QW9+uqrkqRXX31Vn/vc5yRJL7zwgl577TV1Oh3du3dPb7/9dmT6/UJReSkM\nBqEgQu3oEEWSknCI4RR8jg1hA1g4jC3GC0OOglQqlRA6FtJDJBAdSkN/PkYUPgpl9mQGIRqcEzMX\nEWQMJOPgvB0WROg1hpTz0LON8OVyOdVqtTAMJOAoqsYReSttLpfT2tpaGDEGecA78uySoqyG8F5S\noDuMKu/CM+GEuCdJL95hdXU1OET2S0o4eRytUxtwm5LCeIMsSKpgkHgOUAeIZGZmRs1mU/fv30/V\neoLkqAHl3XO5XNQZkvVGCXnHfr8f05A8ymo2m2o0GoHMQaZezuTDZgjVG41GqmxLSk5IxanTxVSt\nVsM4UMM6NTUVE8E4Igc94T3Jgjv375Ed3XDvvvtuFO4fHBxEQqbZbGp9fT344mKxGF1Drn+E6pTh\n4TxI7oCC0W/WE2dMlQCJTA4t9Kw7sgItuLu7m5rXQK++y5pXO+AIcNxPcv3S6U/37t3T5z//eUkj\n5PC7v/u7+uIXv6haraYXX3xRDx480PXr1/XNb34zxql96Utf0l/91V8pn8/rq1/9qn7rt34r/aWZ\njF5++eVYNNAExgDP5eG3oz/nDREOR4QoD/fkM2wO9ykURkd/LC8vx/3ogHGvR9kNAo7x95Bh3NMh\niJIi1PbicylBZM7X8axkORkSwrvxPIRghE18RkqfPuDemO/yUJZ1HQwGqVM3MT4ovXt/KhS84Jsm\nBw+h+D5JqYSCO7FKpaJarRZUhD8z+3RychLdQITM3BOnQJhNzSyGiWdBuaEV/L3YIxQQJIMM8l0+\n5R0jTRjfbDbDMIDciRRwwK1WK9ZhvBPNQ1RfM+dYm81mtCP3+/1IdMHjI1OdzmgiV7PZjMgLWSUi\n8WFAvDf3I3/QarX0/7V3bzF6XtX5wNecHMfxeDw+JrFDgpwTAQoUAoibqqUBVYIUCkINFSCoqkpI\nCChqe9sbSBClBXq4gzaiEqFXQKuCKEJVkYBQCO0FkcqhJjiO4ySesxN75pt5/xej3/6e7435xwaa\nKda8kmV75vved++11+FZz1p7vzfeeGP9z//8T0PfAhoHrlB17ty5WlxcbI3tCsOeYYzz8/Mj1Xd6\nlY6NjSZHjgKqqgZUgA8FXeiZLRifYO++7FhBav/+/a22otq/sbFRn/jEJ55WZP9p15Ydo/eBD3yg\nNddmgSgr09CPyM3Zrays1OzsbENsq6urDUlJKznKiYmJxod5tm19lKrrhu8qr6rWpsGgGX4iI9fE\nxEQ99thjdeDAgbag+CLVdAUIVAVDw63ZF28MnG4qRzpdRR8R2O8pogjvHT5ZpczDL+yl55B37dpV\np0+fbs6I4jo5CXfFWEV3J+7o/TRXfbycFRQ2PT3dXr+7sbHR3uEukEEMGfT8XiqYiFgQgOZUcdNR\nTk9PjxRi6IgsZWVlpR599NF61ateVY899tgIospiWP/8ASk3Y923b9/I4dEKYYpCKSebHxLxp85C\nh1lUWV1drYMHD7bWKsFh586drf3noYceam1aWpZ890LUShYizfeJJ56o9fX1p60pp1s1bBl68skn\n6/Dhw+0NCnYy2baaQR6Pr8c1ayN4/mx9w6tnb65sQpGIPUvnrZfdbjIEsudXUCTJWw8Gg5qbm6vr\nr7++/uzP/uyiHemWHlqC0K+qBqX7fX1aEqqGhR58k8hscfBbHKwDCRijVICj5rAoq8jH4eH2FBek\nf8ZSNSwGQQ0OrzUvxkzpKU3Xbe5M2rVrV+uT83y0B2MlL9E6+0YhO06X8Wcngu8q6EjnNI8r+qys\nrNTc3FxVVWvkr6qGdrxQLx1jnmGQmQG0kcaJ3F9YWKjZ2dmW1ueuKRxj1XCnEfSM1jDX5PNs6lhd\nXW1Fhixa4oEzrfUZjujGG2+sJ554osbGxtpJULhG86ZP5uvfWUxaXl6uM2fOjBxebr3sYlIc00om\nQNDhrutqz549DXVl2j8/P9+Kg6ikiYmJOnXqVC0vL7cDaKwv5CvAkyckmBkPOm16errpsiyEnqU9\nbmxs1IEDB0Z2NQm2WfSS4aBCOOfU1T5IoduCNnnRe6Art/Vmt8dDDz3UNlg4UFogFVRlB4PBcIuv\nDpVLubb0LaJVQ4fk72wV0uAuxeIosoqZ9IBWqKzAVg1Ri4pe1bBybaFUFDOtlD7PzMzUqVOnmoH7\nHoREcSEdXOP6+vrIy7xy3lJI864a8oiJlIxLCi8FJhPooGq0X5RSpsMxL/fmLDnyc+c23wnv3E7P\n5BAd4MxBQVm7du2qw4cPjxSIshoOTUnpDh48WFXDt3JKxRUd8lXA1peBQDoMXKq+vr5ei4uLjSNE\nzUDVHEYGJLIQ4PQcnj9/vh0rODc313qLOTNBlbwZNxkq/kDxHBQOV1BItIwW8Gfnzs331eu1dhQk\nTjGLMouLiw09JpLv28Xk5GTjsc01iyv0JF8d0++DdukGmZiYaJQLwOL1J+a7urraDhbiWKF4zkwK\nbkycNP3LzSoTExMjGWN2tdjtWLXZpD87O9uyFAFz586dLVuir2SU2eelXFv6FlGCl7okj5M9clLu\n5GhwVyZOgKqtUiR8j4iYhRyoiVG6OET/npqaak3GFjL5QkqLD+NApXdVm207XTfsM0zjFVEhLYic\nkhgrhTR/Ud+YkldSTMoAZT7kajfI1VdfXXNzc23PMm44NzpcqANBNZtztQYcm2qwlqk8co+srPue\nPXtqcXGxrQHkkG9wzaovHSIDTgYlcv78+dbqpNKcfaMZwPN1I37n+7Ozs+0kI0UcRQlyScfF6ezY\nsaMdiiML0LXA4a2srNQTTzzR0k8pvXYofKNAah0dfwfdee0NveUk0QacoPQdqsbnpj5NTk62QA3x\nadPKwJE9xZ6DF19fX29H2gnG0unkRy/UM+0+7BrVhfOG+DlchxbJIul2bpQRsCcnJ+vw4cMNMMhe\nU26yg0stNm3pW0RFeaRwphwiEZ5FmpD9lD6rWgo1cRbZykKwFCaJeIZNeaRTnKVouG/fvtagzYA8\nhzNGBxif171KFaRrUE1yU1U14lDzeL+q4etMVL4TcSdZX1UtYEB0WZTzB9+qiT3HDslS+Krhie+o\nEGPMymxymnmIR1W1d+FkYQ6qVhxI2VTVyC4sc2RMCnGcgXX1HciUY6oa9h5CUIIGRyWoZQDHoZ89\ne7bm5+dr7969zdnQjeSOq2rkrQPptCGj3bt3Nx5TAUkHCqpFe5Hquiq5t8DafUR383AV2ROboge4\ncbrNocrQEp16Br4x1xfocSVPvH///qc19LuPtekXickli7WOABwMNl837cjBPkjQWldV7SWIgmtm\nKNL9qmrtZnaXcegKwnT2Yq8tc6RSLsZogTg3nGYqCoNJ5ZDmZbM6p6nCS8Gh3KpqxsABZorHSSQX\nZCFsV5OWM3y7bKRhDDy3vGYrR9Ww+khB0QXmmFxSVlghv8nJyfbGR46GAz179uwI+svWEP+Hdjjy\nsbGxho4Yoz5RwUmKqHkfOu+356ST1NZiW+fY2FhzlpxoNr3jJ53yo1WqqpqOCMIcBEqo6zb3YM/O\nzjZjTx5VWq3Xt6pGGtCrhieP6UKA1MxtfX299U4eOnSo7ZRTyc9uknSy/m2LL5nlQdLJ983MzNTs\n7Gz95Cc/aQhWapwdHpyPtcksLAOPNRwMBiNnsCoYChyCe1WNdCJUDTlKughUoBhsKXX6GltkhzJQ\nNA2+0xqpDwAze/bsabvYZJWopLw/1L579+7at2/fSBdConycfHbYeEuGNsHFxcWRNqyLubb05XcM\niPKlwEH0qiGpzdm6h/RHGihFkCZlkUKqz/nkjoasWnoulMARJILG5VBYFUTcbbZvGHdyLhSAIxBU\nUBx9+iL5KcjP8X5I8pRVFpBwplXDVJjjMP6UG+dA0c2JYuYhw7kuni1NyvafTAkh3EScaWxV1b57\n9uzZVmVNGmQw2NyrfvDgwZbWQT1Sc2k03jnnkOiWDtAnhbgs6mVQMxbySU4+1/Daa69tB5VwFElH\nXYjDhADHx8drbm6u9u/fP4LeBFtjzxRZexd0nV0H2colFadzAgPnmDRTBiL3sCbWWJFsaWmp9u7d\n29Bi8s8CBqSZGY95kAVknQVcjjfbCh2raA7ZzWGrsOdXVZuzrAsyzixrbGy4PXptba0+9rGP1cW6\nxy1DpBsbG22veKZT2p5S6EkEp7Ksr6835IXDqRpynJQAquGEEulRGA7JwjpQhKJKB/GUyb/Y7ocM\nz5PRcT7JYSZ5nsok2lMshmpOLvQAJ5Yvz0unVjXcc8xpSFPzGaq+eNOqISpJY0hu09qkQ2DoChE5\nfxlGyoGxkQPUmYU2uuL+2XnhflNTm+cncFhkD7VrNTOWfFNt1ZAfrNp0hHnACbSYTf1ZkPR/6yo7\neuSRR+rEiRMN7Ugbz507V6dPn27oKbsQZERPPvlkO0wkd+dk/y7HiYtMZ9vv9EjHmHNHb1h7a5G8\nKuDAaQngZKcXO/nx5GO1yBmTzg+66RnqH9LwrA9YU0f67dy5sx5//PH2DOsgOABC3sflEJvMOvkA\nzpT8yAfIuNhrSx1pVbX0l0JRLgpcNXxHEkVVNecAxsfHW19YokGLamGSh4TKkqd0qnfXbbZ/LC4u\n1q5du5rTXlpaalsuOWYLuba2eSDEk08+2XaASOOyCmhOFHt9fb2hUHPJNDdbQqTbGc3Jxjyk5QpF\ngkEWHkR06aHteiqnCHdpIMfEYVBsczLuzASgdoW3RBM+K83LTgWv75ByQnG+n2l9ImBFuDQ8dIKi\nBtSS9AVjJ9/JyclWIVfsQiek0zcWz/Qzchob23wnk7MToOgdO3bUoUOHanV1tb2HicMjzzxOMk85\nS32qqoaepa/GTD6cIkQumNqxZZycOD2nO5kR5Xm28/Pzjf4aGxtrZ8uyLcHLeNmuoK34SG9w1YAR\nJGyeSblUDc++5fzw4agyx1Lm6U44dTudlpeXR7IkQVywzG6Yi7m2tGrP+EXXdAQUR/TNxSF4Vbap\nqakWMUVsvEmmQpyf56WycSbI7XPnztWhQ4dasYriHTx4sC00w+IIzp8/X3v37q2pqanWmJ09i8mV\npuJBp1U1grbs5qkaojKvRkmEmVyl/9twUFUjFWYFJvM0jmxU9kqRTLn0YZK7yjlDIBMoXWDZ2Nhs\nO4PcoQbzdugIJ+bw3XTemalYQ1sEySHHkDqiK8C4cz04LzIj3+ycMNbsDuBktNBwXMmFQj/auqyP\nNeCUOEp8YaJkVwYTAdH32BHwYRwCBCSdnQqeb/2zdxtKRYHQW0H5scceq42NjTp69Gg7/Sw7Ctyf\ng9a5kp0W7tnP0DzPz80dSk0ELQvEs9MPab4sCLpkU2gIvcZJ3zlXNWmYi722zJFm20hCbQoGRU1P\nT7fol5EIsQ9BUpxsN8l2i0wjc4EZaRY2LLqTsrPpPRc8iwkck2iLB6WgorLUN8ed6WhyU4PB8D1G\nUpKNjY2GiHU3ZOpqjvglKUqmn9pvyDlR8JkzZ9oefkayc+fOFiBEdGOAWikjp1FVbU+2sXOmWks4\nIAHMu3YScSc/Zx5VNdJLi/8WhKamphpfqtosm8kMws/pSBZxUBxSTs9dXd18v/rk5GbzeTrx3FRB\nByF836WL0J9zF3IrZdUwQFk/c8zipDamquEBOOR2oZqCtiXjFZRwmvTT2PI1HD4va5ufn68zZ87U\nYLC5Yyi3nWaQp1/AAj3RGmZekDZZVVULSirrbERxdW5urk6dOtXWnu/IghyZ0VXj8cI7/mIw2Hyv\nmPEllXYx15Y50uRO/CEIKVhVtZSFQqmCm7x/M5yq4fvDfSYLNRyYRRGlVPWyaGXHFKRx5MiRmpmZ\nqYWFhdbOhBRPzs0RY5RY9Of0BAYOSSRfXV1tyMl4KaSon0UmMjNXTocMnL/ImDl9TgRS8G/ONt9+\nOj8/307z4jQ4nkS0DNj9x8c3T+NxlqdAJlAtLy+PFH4mJibac7IwRzeksJrs8dkZEIwjCwzeDaW4\n502dgla2gqXhpfPPIszOnTvrzJkzVVUjKXJu+bUOxoRS4ZT8O/+gmtKxcDwcWhZptCxxGIJTggpy\nTUqCvvrsxsbGSMuPOZuDdYXcyDf5YgUcB4p4ZlIK+EfrkBnm6upqK1QJWlnYM/5E7ZDmvn37RvyK\nsXs/VvbJZhEMMJI1CbD6VS+2yOTaMkeaJ9DnnmxCZowWJCt3Wemv2hSSfeXQqkXXt5gG5lR50Q7i\n0rPKCVoUxZyTJ0/WxsZGezc8BwbtUOysCnIE3nqaDcOcqT44ypUHbvTTIugs20SgewEgjWhxcXEE\nRWrrgeY4NBHba5ilTVdffXWbL9SlGVvwYNiKEJRweXm5GUFVtQCyY8eOdo4mJJaFkKphMRLCmZ6e\nbg5ZEaFfpGEQimYcD0SVdIR2HzroFSkCln+n81fdzVfQ4HLxbRA4Lrtq6Nz933olekRpJKo0Vk5W\nAAAAzp071wqbUtwEBRMTE0/bf07H3Mf38vIql9XV1VpYWKjV1c09+nv27Gl2lzx5VtgXFhYa9QE9\nuj9aamNjo+3h9zkBl3yg9qphld/nyUH2py+XTvujp5wtHDhwoJaWlhqPShd16Djv4me5tqz96Y/+\n6I+aU0ilqRpyLZmaSDeSA8IxaT2C3BLFuUeiwmx70MZB2BSzqlrxI9/uWTX6jhlKwBG6LKrP9otN\nVcPdVpys33O+vsuIpIsOHCY/n820J+kHf6Si9vcnr0jeZJ98q7QrK8yC2tLS0kjVMykN/YeZXvad\nG0TkferWlYFbp3Qk/ub8U4eyGLOystKQSbYH+ZPbNFE85mgtcl04QygL4oKYs4e3T0UkjcIhQ3/5\nWcVUDsBmEnQEvVLMyyJa2oWAnc9MvcyOg2wfyrapLJJat0TVqJosOrIhh7uYm99Bzu4B0appQN9k\nndui0Wq2bWv7MyY66blsIdP8HB8qkD7aOgq8/FK0P1VVU26LJpJUVZt8pj4cTNVo+scAqobRnpFC\nr4TN0BIxQnOUOiuZtnb2K4gQ2NGjR9vpM4ybgSRXl9/NVCILDHoIc47STP9fWFioAwcOtP9zVE89\n9VTr8cyNBhRIhXdiYqI54qrR7bBSImOwhzu7IygWQ9AHm6kc9Gfu6ZSramStFAenp6fb6fd0gvGk\nU8VdWjMyZGR4PwEDteFn1pyzy5QYSs1WJ5mL59NLBk32SQlA9cZun7liSQbqDPTOgsittDmfLNDQ\nCWssZcXVc0hkJZhm0S3TWTI3JmPM4lsGxORA0S05ZghdUIDejTP1nCOFVgUq8krOVsaH2tm/f3/L\nXpwUB0BdddVVzS7ZtAOIcgdcZhyHDx+uvXv3tuL1xV5bikhF2ORvsrCS1UBRNYsxVaPvu8l0I1N5\nbRc+kxFf5HZ/vGgeQJG9p1kM0VHA+bt3VY2MP3/PaLLam/ykcWeFVVTP7gDpSMqOgXie9pmqGtnj\nTDGnpja3HGaRyNyqNnd/2Y5K5v02pqz0asNJIzCfTNWqaqSyzygV4qSSuLxsUeL4sjjptC1pfVW1\nI9mSb+M4E2lVjZ4La93823rleKpqZC93Zk0cLp7cGK0/mSUqzM9AWOaZPZuZ2WQQyJSdTpGLbaN4\n4uRfUWCZSjvmUPExi4O5DRqfeSHwMTEx0Rrmrd3CwkLrZEl9N3YIVEEw+VCfTx+Q6FYBWkHL+mZA\ntZ79jSI2mdBrgXTXrl3153/+5//3EamKM+Fo3cgKvT+QmZaHrJBSuoy2eSwZPg7CYIBVo9wVB6Cd\nam1trfGmTqHynXT4nCPExmg9gxPOQgxeLR1bpuWcGaWoqpGgkieQQ9feKpCpGUeAc00naWwOnqCg\nlHdpaan19GUAsyauHTs2T3fXFqOIaB0YChlwHuSVKRn5uGeeTQk1QdjWsqqaPNNxuT9HBGWigbLQ\nI4MRTMiIzuQa+BxdSzqJPKCx3BmWKTuHUTWkUxJpJgVhHL6Xnx8fH75rHgdqrVM/E2y4l/tyaGNj\nY/Xwww+3AC0LyeIZO0tePoEFebMr37WT0MEvdDOd4tjYWOsF1zWSSDJ59qyqJ9LOdB1vj+vPorY1\ndpC5IO5zSVdc7LWlxaZMKSk6p5I7CxiGbYN9vtKeegKQ2lZVO6eU0TIen83WGcK31xYH5dUSmbZX\nDXsvOf68su2ij0irhq+j5nQSpSqQJRdr44K/s6rLwBn2YLB5WDQl3Lt3b0upsujhs5BKon0OAepO\nh5q8FsP1vqmqatxc9uQld80gpcDWxvmXnC/0KHXMSnGm6llNtv6Z1iZV4p7QexZw8t6KkO6VnRn0\nIg+egXogGrSHdDJP/YIEM0WvGh5qQq5JbfX3h3sWCskYk2O+kEPFrwo6dLWqWpqM802HSLZesyw4\nQn+Qb/9Q8+TNk2/3/wywgiUUa105biAnA9jBgwebneWRf/h5BVWbInSsKA4/9thjLUApjspCL+Xa\nMkea/Z0WOAszyQchnPGSjLiPDLJiV7VpFGngWXxx5iVDl/p3XdcOL+jzJ6qEVdVahKQJiW4pfqYk\nVcNeQM9NHtj4/JtSUWDPSQ6LYU5MTLQDSKAE1Unb41Sqs4peNSyCWAO8cp7ExEAzeKWiJ99WNboX\nOgsAVdWCV3Y8cBDmmo34nJB+3kSSHH2OgdGZqyCtR3V8fLw5qeS0EwVyrPi/LOYZVxYq6VRmKPTH\nmln/DMQMXmrqgurxnemgjM8BJilD+mQd8zCbfnosWFUNtxGztSwEVm0CFAgVFeOeHCgHPjc3154N\nxXpmAprkXY1xfHy8ZX9JGdDFvr1sbGy0zo/0K0mxoBncW0Cze47tW1ey/aXZ2WQxKGk6yqrhQSL+\nnYRzIkCRjQJIdWx51JuZiDTTTUrjlRBPPPFEUzAC5ww0uK+vr9fJkyfb+Znp4Ch03/FkESX3lOvv\nhCjGx8fbGQQ4pOT4+pybgkvueILKbENMOXIWgoax2APtDM4f/vCHzQGm00puMp1XptQKP/l2Ss9U\nvCAPTlQAwGNZ8yuvvLKduCV9hebSeUunycArgwUXLXbkZZz5Co9sfWPYWegzF/O2PtLXdKJVw4o9\nJ2DcY2NjLYXGYaMMrCvEJZBx6lXVELB7JwLOte0fSOLzCi6cSbbC+X+Og07l2lQNHb6f2VKdAUXR\nB31nrDIu4zU+9nD+/PlaWlpqaNbW8aphkZIj9/ONjY3Gkwoy1gPKNCZr79U7fIhsaWZm5pL82ZZy\npKkE6SD7CI1iphJkxGSIFEY0clEozg0VIEopJohKFIfCJ6djAWZmZtp2SlwYNDUYDF+RTLmzzUVB\nRBChpLZm+j3aglFlYSlTpqohf5dnNia9wAgoLXQirZdWnzlzplZWVuqFL3xhnT59eoRTQ0NkgSKd\nKEeYp+QnbysQOWhDmpuUS1W118v4mXsPBoNWAWdwuV6a7VPH9u3bN4IuGb4gwjFaIygqe3ddAnnu\ncvN99AeqJCvnEBI9QW9AmRngrUsGY+ttrvQQZZIB0pjpe2715MC86YDOKPzZMy8AZIrsu9k9sLi4\nWMvLy+18VHZlrdlhOuTsVMjipgCjF3dqaqrRU7Y064+dmppqabh1XFlZaQXOfGFkBp8dO3bU3r17\nGwp9/PHHR3ZSpUz0Cl/sdVGOdGFhod785jfX8573vLrtttvq/vs6WZoEAAAgAElEQVTvr7m5ubrj\njjvq5ptvrte85jW1sLDQPn/33XfXTTfdVLfeemt9+ctfvuA9LT7jJGgpH4RIgauqGV1G4KrRAwbG\nx8cbmst0vapGdsf4WbZHSQVU75MPVHlUuPBzRLpITMlnZ2drcnKyvY+Iw8YPpbNg6Bw6pJvVXdF0\nbW2tnUxFZpC352e1WiWXIXOeDFJ1eXFxsSmdex8+fLjW1tbaqyOg7TRKp24lYk6EnQjHvb3GQ6or\n1bbO7pFFRO/eQZlMT0/X1NRU7dmzp6FIKCXRF5nkds7MZpKrY9yQEqNNp85ZnT9/fmQLsrFm0NAD\nmnIQnKuGZ6gmz2s3ThZiIfIEFP2+XhlYVbVgce7cuZFTsTL7yoJb0guCYYIUayLw+ZNvIEiqQFZl\nHDt37qyDBw82ugXPap4ZhAERWeXGxkY7fMRGEuOSCW5sbG6Swb/KwqyDCv/ExES71+rqansFOzsi\n46eeeqqeeOKJi3GN7bqo9qd3vOMd9Wu/9mv1rne9qwaDzePmPvjBD9aBAwfqT/7kT+rDH/5wzc/P\n1z333FMPPvhgvfWtb63/+I//qJMnT9Zv/uZv1ve///2mgFWbEev9739/EwbhM65cuD65zkAh2EwT\ncHkikdYkwidon88CAEOsqpayUSiOIiNsIhW/W1lZqWuuuaY5Np8x9nw/jFQR8uAgMxL2o7X0HcLJ\ndiFIMKkLUdrnkpNyf6ljcqxVw8ZtxaalpaUR9M34du/eXU888cTTGtoZhjXWjiRwCJTQV3YVcC55\nkntVtcKTAFE1DKLkg0fsn22QjqNPT2QRyTq5Z3KMyd0z9kRXOjzwqrjDnTt3tsNL+mtA1/HGdM3z\nqp7eapWoN1EjeiQphr7t6O8FGDKQ003cb1IReEu72jjB5BkFDHZD/g4IZ5NVw1aqPgKm44pDeQ6C\nNaXvAIMxm1dmkQKhsfVb0gRLY6FTO3bsqL/4i7+oi3CPm2v4TB9YXFysr33ta/Wud72rGdjMzEx9\n4QtfqHe84x1VteloP/e5z1VV1ec///m66667ampqqm644Ya68cYb61vf+tbT7putIIRm0hSQk8Wn\n+R202Of88qT8/l73bGugoEmwG1PyaRmh0QAEy8lT2PX1zdO3GWPyv5lyMQDoYX19vb0DSPN/Fj5s\nBVQZh3gpjCJINv933ea+5p07d46ksZleMbLz58+3t1qm09bLqS3LKeWZqi4sLLTT4TNAGDfO2+4z\ncuYks5LLuPwMknBxFpOTw3eUc7xVm5mFbasMKwtJWVipGjrgTLfHx8db9T03YNCf/D9kTp59HbLm\n6KhEUsmd+x6uj264B6N3wIcrdcS/6XQGsdx1l0Uoz8/imyDqcm/ZgeIjWfmMXUayyUTw7AAfyT6d\napbyUFQk6yxMcW4cqK3KGVidI2v+SUNdeeWVI+dckJkslx5eqCB7MdfkM33g+PHjdfDgwXrnO99Z\n//Vf/1UvfelL62Mf+1idPn26Dh8+XFVVhw8frtOnT1dV1SOPPFKvfOUr2/ePHj1aJ0+efNp977//\n/jaBI0eO1A033NAidTZdi+YmLk2TFpm0lF+hRgRD8lsoEdkiK/hkm0vuqOHU7SWuGhph9uZVDRFT\n8pcMWiXcwrrW1zdfbbBv375mTBzI0tJSO2cTIkhiHfr0DAhB5Z3T0Vg9GAxGUnQRP5uu3Z+BkUHy\niJkFpENLGsT3pOGZIlYNU0ZIlrGur2/uLllcXBzhebN1q58pMC4HDK+urrZiUxZKrHkWULLdzVmZ\nWcCiC1m08GwISGdB1SbQuOqqq9qmDvPq8/TkkFlU8pycs9Yvz4UeyRd6zMZ7l2DLdtiEQ6PJFjDJ\nE8my6wAPbG3Z4dmzZ9uWZYF+fn6+nRtAzwRxtsruUC0CiM/SQSAB3QFFkpe1oRecbdWQp80t07pb\n0FH4Xnpw/Pjx9p6opKMu5nrGTw8Gg3rggQfqr//6r+v222+v973vfXXPPfeMfCYLBRe6LvS722+/\nvSlB1XBrpyhPUSCMbO0A0zPNojAqhBYoU0eKArGmw8sqtIWuGnYXaH3CKTIMqMtCJ11hHkk9ZCGJ\nfI8dO9bkhCagCC4GYt7pcMmHg56YmGhpLoW1LTSVmYOBwHLsnpcIb319vY4dO1anTp1qRr+4uNgK\nR2TKSWWrSR4GkejImjHWjY2N+tGPftTQi2KXftV0AKlbY2NjdeDAgRbY0DiJavwNZRmbwzjyDAPp\nNjSeASQzmnReKAs8tHs4GT85/+QUs21O8MkA0M+ExsfHG1XC+aQszM26udJekprI+/oMW1xf33y3\n18LCQu3evXvEwfbpB9s0yYR9Z10hA7JdTBnoqobblrNolr2/WfwyxuyVlU3Nz8/XzMzMyKEvyQkv\nLS1VVbVzAa6//vp67nOf28byjW984wLe7MLXM6b2R48eraNHj9btt99eVVVvfvOb64EHHqirr766\nHn300aqqOnXqVB06dKiqqo4cOdKOQ6uqevjhh+vIkSNPuy+iH6dVVe3fHKbDa5PXSFSUDgSqETVF\nffC+qtpnOD7ohiOWJnAG0Ki0G7GugFBVDcFsbGy005WQ7BST0VwIFU1Nbb6qYWlpaaTAoZ8tjQLS\ng6IpXLYOccTJASWNIt1X7T116lQbo+8lx7l79+7mEFV7q4avddi/f3+rUHMCWaHONA+yZHxJA0B1\njz32WM3MzLTDRjgfAaiqRpq9szOhv9uIcRsThNl1w1PBID0ZB4fVdV0bk4wleXx6RiczXabL/p1H\ns5GrFFmQSZqLQ8l2nXRwgg/9EswTcAjggivul46guRREgQ5j9EzAReM7Okk6zalyeKurq60Yqj0K\nFWPOp0+fbnaLtsJLkwP9Jav19fVGbSkkZ89r6h993rNnT6MtnnjiiWYnKK3MdJLu2r1794i+Xcz1\njI706quvruuuu66+//3vV1XVV77ylXr+859fr3/96+vee++tqqp777233vCGN1RV1Z133ln33Xdf\nra6u1vHjx+sHP/hBvfzlL3/afVP5oSzckKZcDstCcLgmmQUDQgX1kwvFlzIEC5SoFmK1CImyOVsR\ncmNjo7VYOFtzdna2LcDi4mI9+eSTrU+v67q2p9crSCwcbtUWOor/1FNP1YEDB0Y4I7/LYpfWG/c0\nT8pHcSi2CrxXptj1lBlA1TAtVCBAFzz++OMjxRWOPw+B8XtjhPiyIixY+J5U7ciRIw1lkVkGxiuu\nuKIOHDgw0oKURaGsvkJIie7JMnc4ZcqdvOXk5GQLiisrK03GHKmgbyceVD4xsXl8Xb+TIv9N99AJ\nAgxnl1lRji937XGYgmry8hlABXF6nYgxn8kxZ2aVlWxolt6SUfLR6TDRdA4O4VT37dvXaDuBUCEp\nC8xqJnNzczUYDGpubq7puOP66BtqReAUPMlqZmamUW1sCHpOioTtZRvdxVwXRQT81V/9Vf3e7/1e\nra6u1rFjx+rv/u7van19vd7ylrfUJz/5ybrhhhvqH//xH6uq6rbbbqu3vOUtddttt9Xk5GT97d/+\n7QVTeyiNk/M+G0LhHFIppHWKG9BAGkEf7XEA2eifKYnPJGlNcRi7e3McjtuiePv27Wu9eHoHcXX+\nHDx4sC2ek2UGg82Dk6+55pr2DI3Dy8vLTeEgPpQGp7S8vNx67RiWPfdVo4dcJC9rfFXVWrFUyCGC\nNCwoTTFHYBB0GDFHmU3SHJ1i4tmzZxv90u/3tXa2Z1Ztno155syZOnjwYDNMSp5ODbogO7LMQ0oE\nPhlMFiYZpMJcdg7oT3U/W0SzSu7/eeC3NXBPznIw2Hx/k73h6TxWVlbqwIEDTyt6QHvWWWuVNcRD\nJjdp3TIjMc6NjY2RM1Ppd7bVJa/JJq0x7nJ1dbV1c/Qps3zVTlXVD3/4wzp06FBbKzQZ5yf19ieD\nThb/qoYHEdFRaJyM7Kzr1zzwo4K9k9uqqubm5mr37t0tG7qUa8tOf3rve9/bjA8ZT5BVNfKSLD/D\nc1QN3/teNWydkqoluc2A03Dt6sCNZguG9CXbnyhTFkqgOzsjUgk4H8oHfRrT2tpaPfLII/Wc5zyn\nzZ9DgNAFlUwJGa4xG2sW1RDufQ4rETmHSVmhI3Ji3GTuvgyGPBhf8tDJdXk2Q/L/NFTP8hZKfJbP\nVQ1PfBJEOSOO0RiTNuHgydUBFRwkvUgUZ/xkkf2NnFNf/r6f1EofIWeAr6qWRWURJAOWNNx74pPf\ns96JiFXLBQwOVjsW5LZjx44RB5zjI9N8VQidg+4SrUOmieaSdtNOlxQThEsHIfjUE/ooOKDhsmZR\nVa3gnDvNZI5AF8SvQJ3931nUcgmUVZsA4G/+5m/qYt3jlr5qxGLgQCGEycnJ1jjLQaQzQvJzGBAS\nYfu5SF81emoTPowDSYPhHDgk/X0Un/HgdiBRY6MsGxsbDS2ura01504hHXhicaFgaDMLZv2UPtEG\nRUrHkUWfVC5KrUMhOTHRmQzNI9GXcfi3MVJknBqDc7JOvsCP44UYBScyRAlk4MKJZTdAv8LMCZCR\nMbugFaksXWO45JkFqgzUAk+fNkrk7j5amMwle0dz/ehSUk5kad5Z+KGbyX1af3+si0Z8cyb/bM2j\nk76DJgA6kqvEqeOCBS12R//MDYo3zqmpqbYrzffJNwOE5yQFl1RHPyNl48aciBx1YHyJvpOHtva2\npGYwuthrS19+VzVMrbMSmHyFhnCpQ9XwPU6cWVYF+06QoUkNRC7P7Buyn2sep0icHKefCJZBKGyl\n0fqMlJ/S+j9jwxllOu7eOT9zMxYyU2GenZ1tRixwUE7/f/jhh9ucOQnOSmCAjrXgzM7OPq3/kMJl\nQLBuGrAnJzdfzUHR8WS2VzKGfq9nppB0wzpIvXPXkLM33YNDtCa5c8duM8bps9JnaKmPUKuGL6Xj\n7LJvmJwnJibaOuR9rRE96FMnWVDFh2Z3hwwkgzW5cHIQO26dDSTPnLaWzm9ycrIVbfRq5uYEus3h\nJKLWZyyVzgDjs3Y/pv6Qb78rhY4DBtlpgOf3XN8Djhzona2OAq1gYa28opmz5uwzCF/MtaXvtVft\nzCgllc70SLqbqE8ErRruTlAgSaRooSw655HEe9XolrNMUwaDzRPPRcHc7ws5WexEDangKp9JR6gc\nJseYqISBZjVUEGCgDIJ87B82D+MXDDznyJEjIymb1C+Nl3GPjY21LXfS6ESMtm6aF8eWjiLTwuzW\nICPv1sFLJx3BAVn7qmrOuqpa4z756BuUdXAUHHnV8LANcsmsxL0EzCzCeb7gbB55XByawHZi97VO\nuZkkeV0BLdNjmYy1wzsao0wMCvbzDPT0yzrYT88ZJ2pcWVkZeWMn3RGMk3KBIDPTov/Z6iVQTU5O\ntoZ59Q+0A3kaP2fq52oTgq/Ax+F7Npumz2l/KDSUDhCWrWf9g1wu5doyR0rBpY+Qg8VijD5D+cfG\nNg84EbE4yL7RVg0jH56FsYvknEI64bW1za2aa2trLdUVddNhJi+6vLz8tLebUoIzZ840pRL5BoNB\nqwxyEIxXlTj/JAeJ1+MYpDqCkNTZO4T8jiImp5cFDcpGxoyh67q2JTB7OLUDebc5OWeK7AzR5BPn\n5+dHCixpzOQ/OzvbZAtJcLCe0T8zQSrPuWgn4zCsXXY3cKQcs/sJvpBQGlZmIDjA5GbpJFoDUMDf\nJ4VB76DBdKpnz56t6enpFiisDb3KjMmY2YS1gCazre2pp55qmYVggJd2ZkTVMIAmZcHJHzp0aGSz\nw9LSUuscYHfmaUxVmxX1lZWVkQKgrCLntrq6WouLiy34OEIRAiXnqhrh/FOHBTYySvol1zY7Xciw\nv2nmYq4tfdVIpiZJzlcNtwSmgEQckQ/iZIyimHtKgwmUwuZzIR5CvZDRUKRMSaTn2QisijkYDGp2\ndralxubgMOtz587VtddeO9K6Y+95pmGeUzXcRWW+VcOiBafZJ/F9xtghMtGccWQxxTpwQiK5Srpx\niPTpCCEF65WprWdALBycNciOgGwjYnQccqJDcs+CIQPLjAOdkm1yueWxT/FUDc/L9dwLFRPTcdAX\nnyF78rVOKaMs9HiONi1jJkfI03rk+85ynOzLRYeykyPrBlWjL8TLbCaLtdlWRDeTO+bczU2Qysxs\nMBi0nYcQY65fUlkJeqw/h5/F0qRu0hZT5/uFULIVQD0ji9RVVR/96Ef/7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/qkLJBdyrWl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\noJQ3q/OpC8bFkD
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